Polygonal fault systems(PFS),characterized by multi-directional fault patterns within layered sequences,are well-documented features in global continental margin basins.While the geometry and formation mechanisms of P...Polygonal fault systems(PFS),characterized by multi-directional fault patterns within layered sequences,are well-documented features in global continental margin basins.While the geometry and formation mechanisms of PFS have been extensively studied in the northern South China Sea,the PFS in the Zhongjiannan Basin(western South China Sea)remain relatively unexplored,with a lack of quantitative analysis regarding their propagation.This study addresses this gap by using high-resolution three-dimensional(3D)seismic data and conducting a quantitative fault analysis to thoroughly examine the planform,cross-sectional geometry,and evolution of PFS in the northern Zhongjiannan Basin.The absence of a dominant strike direction among these polygonal faults suggests that their evolution is not controlled by anisotropic stress.Our interpretation of seismic data,constrained by the spatial relationship among PFS,gullies,and pockmarks,indicates that PFS mainly developed within the Miocene strata,with their initiation occurring during the late Miocene.Furthermore,the PFS act as key conduits connecting gullies to pockmarks in this area.The formation and development of PFS may be primarily driven by thermally triggered processes within siliceous sediments.The necessary heat source is probably associated with the abundant submarine magmatism observed in the Zhongjiannan Basin.To reconstruct the regional geological history,a four-stage evolutionary model,incorporating the formation of PFS,is presented.This research significantly improves our understanding of the regional geological evolution of the Zhongjiannan Basin,providing critical insights into the initiation and development of PFS in the western South China Sea.展开更多
The Guanxian-Anxian fault zone in the Longmen Shan,Sichuan,China,exhibits long-term creep-slip but ruptured during the 2008 Wenchuan earthquake,challenging the view that creeping faults rarely generate strong earthqua...The Guanxian-Anxian fault zone in the Longmen Shan,Sichuan,China,exhibits long-term creep-slip but ruptured during the 2008 Wenchuan earthquake,challenging the view that creeping faults rarely generate strong earthquakes.To investigate the transition from creep-slip to stick-slip,we analyzed fault rocks from the WFSD-3,using microstructural observations,XRD,μXRF,Raman spectroscopy,and quartz grain size statistics.Fault rocks show intense foliation,pressure-solution structures,and abundant clay minerals,reflecting long-term aseismic creep.At the interface between black and gray fault gouges at~1249.98 m,microstructures indicate stick-slip behavior,including truncated grains,angular fragments,and finer grain sizes.Here,clay content drops sharply while strong minerals(quartz,feldspar,calcite,dolomite)increase.Elemental mapping shows Al and K enriched in black gouge,whereas Ca and Si in gray gouge;Raman spectroscopy indicates possible graphitization;the finest quartz grains occur in black gouge.These features mark co-seismic principal slip zone of the Wenchuan earthquake.We propose that fluid-driven transformation of strong minerals into clays facilitates creep-slip,whereas localized precipitation of strong minerals strengthens the fault,causing stress accumulation and controlling the creep-slip to stick-slip transition.This mechanism has implications for reassessing seismic hazards of creeping faults.展开更多
Accurate fault modeling is essential for understanding earthquake rupture processes and improving seismic hazard assessment.We present a unified framework that integrates geodetic data with multidisciplinary constrain...Accurate fault modeling is essential for understanding earthquake rupture processes and improving seismic hazard assessment.We present a unified framework that integrates geodetic data with multidisciplinary constraints,including relocated aftershocks,geological observations,and geophysical information,to adaptively model fault geometry and slip in diverse scenarios such as multi-segment and multi-event ruptures.The framework combines adaptive fault construction with a scenario-driven Bayesian joint inversion approach.Fault geometries are first built from prior constraints,such as surface ruptures and aftershocks,and then refined through probabilistic inference when such data are inadequate.To enhance computational efficiency,we introduce a Sequential Monte Carlo Fukuda-Johnson(SMC-FJ)strategy.This separates nonlinear parameters-including geometry,data weights,and smoothing factors-from linear slip parameters,which are conditionally solved via constrained least squares.Geometry updates follow a hierarchical adjustment scheme based on point,line,and structural units,enabling flexibility across rupture complexities.Synthetic tests and four case studies,including the 2022 Menyuan,2023 Türkiye,2022 Luding,and 2019 Ridgecrest earthquakes,demonstrate robustness under various constraints.For the Menyuan earthquake,full Bayesian inversion confirms that the fault geometry constrained by relocated aftershocks is highly accurate and needs only minor adjustment to match the observed surface deformation.The results further show that gradual changes in fault strike and dip modulated rupture arrest and postseismic stress accumulation,highlighting the critical role of inherited geometric structure in controlling rupture termination and delayed seismic activation.展开更多
With the rapid development of large-scale offshore wind farms,efficient and reliable power transmission systems are urgently needed.Hybrid high-voltage direct current(HVDC)configurations combining a diode rectifier un...With the rapid development of large-scale offshore wind farms,efficient and reliable power transmission systems are urgently needed.Hybrid high-voltage direct current(HVDC)configurations combining a diode rectifier unit(DRU)and a modular multilevel converter(MMC)have emerged as a promising solution,offering advantages in cost-effectiveness and control capability.However,the uncontrollable nature of the DRU poses significant challenges for systemstability under offshore AC fault conditions,particularly due to its inability to provide fault current or voltage support.This paper investigates the offshore AC fault characteristics and fault ride-through(FRT)strategy of a hybrid offshore wind power transmission system based on a diode rectifier unit DRU and MMC.First,the dynamic response of the hybrid system under offshore symmetrical three-phase faults is analyzed.It is demonstrated that due to the unidirectional conduction nature of the DRU,its AC current rapidly drops to zero during faults,and the fault current is solely contributed by the wind turbine generators(WTGs)and wind farm MMC(WFMMC).Based on this analysis,a coordinated FRT strategy is proposed,which combines a segmented current limiting control for the wind-turbine(WT)grid-side converters(GSCs)and a constant AC current control for the WFMMC.The strategy ensures effective voltage support during the fault and prevents MMC current saturation during fault recovery,enabling fast and stable system restoration.Electromagnetic transient simulations in PSCAD/EMTDC verify the feasibility of the proposed fault ride-through strategy.展开更多
The Yingxiu-Beichuan fault zone(YBFZ)has long been active and experienced repeated large earthquakes.The physicochemical properties of the deep fault zone(>1000 m)are the key to understanding the deformation mechan...The Yingxiu-Beichuan fault zone(YBFZ)has long been active and experienced repeated large earthquakes.The physicochemical properties of the deep fault zone(>1000 m)are the key to understanding the deformation mechanism of large earthquakes.This study uses rock magnetic,microstructural,and geochemical analyses of representative samples exposed in FZ1681 within the Wenchuan Earthquake Fault Scientific Drilling borehole 2(WFSD-2)cores.Fault gouge and fault breccia have higher magnetic susceptibility values than wall rocks,and they contain abundant paramagnetic minerals and small quantities of magnetite and monoclinic pyrrhotite.The magnetite and monoclinic pyrrhotite in the fault gouge were mainly formed by coseismic frictional heating,indicating that large earthquakes with frictional heating temperatures of~500-900℃once occurred in the YBFZ.The seismogenic and coseismic environment was reducing with a relatively high sulfur content.The monoclinic pyrrhotite in the fault breccia was formed mainly by low-temperature hydrothermal fluid.This indicates that the fault zone experienced reducing and low-temperature(<400℃)hydrothermal fluid with a relatively high sulfur content after the earthquake.The YBFZ,which experiences frequent large earthquakes,is weakly oxidizing environment at different depths,but the effect of the low-temperature hydrothermal fluid is weaker at depth.展开更多
Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)...Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)control strategy based on a power outer loop and voltage-current inner loops,aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions.During voltage sags,the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support,thereby effectively suppressing the steady-state component of the fault current.To address the active power imbalance induced by voltage sags,a dynamic active power reference correction method based on apparent power is designed to mitigate power angle oscillations and limit transient current.Moreover,an adaptive virtual impedance loop is implemented to enhance dynamic transient current-limiting performance during the fault initiation phase.This approach improves the responsiveness of the inner loop and ensures safe system operation under various fault severities.Under asymmetric fault conditions,a negative-sequence reactive current compensation strategy is incorporated to further suppress negative-sequence voltage and improve voltage symmetry.The proposed control scheme enables coordinated operation of multiple control objectives,including voltage support,current suppression,and power angle stability,across different fault scenarios.Finally,MATLAB/Simulink simulation results validate the effectiveness of the proposed strategy,showcasing its superior performance in current limiting and power angle stability,thereby significantly enhancing the system’s fault ride-through capability.展开更多
Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelli...Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of ...The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
This study examines a 1.32 m thick sediment sequence from the Cunge sag pond in the Litang Basin,eastern Tibetan Plateau,to assess the seismicity of the Litang fault during the Holocene.High-resolution geochemical,gra...This study examines a 1.32 m thick sediment sequence from the Cunge sag pond in the Litang Basin,eastern Tibetan Plateau,to assess the seismicity of the Litang fault during the Holocene.High-resolution geochemical,grain size,magnetic susceptibility,and total organic carbon indicators are employed to obtain a continuous record of changes in elemental,physical,and biological properties within the profile to identify seismic events.The seismic event layer generally comprises two sedimentary rhythms:a lower coarse sand layer and an upper fine silt-clay layer.These layers represent rapid deposition associated with fault activity(Earthquake A)and slower deposition during calm periods or earthquake recurrence intervals(Seismic interval A).Through six^(14)C dating,five seismic events have been identified in the Cunge sag pond section:E1(before 3955 a B.P.),E2(3713-3703 a B.P.),E3(3492-3392 a B.P.),E4(2031-1894 a B.P.),and E5(1384-1321 a B.P.).E1-E4 had shown a good consistency with the paleo-earthquake recorded by the trench,and whereas E5 is a newly identified seismic event,further improving the continuous earthquake sequence of the Litang fault.Based on existing trench data and the seismic event record from the Cunge sag pond,a total of 11 paleo-earthquakes are identified along the Litang fault since the Holocene.The paleo-earthquake activity of the Litang fault exhibits a clustered pattern,with recurrence intervals of both long periods(1000 a)and short periods(500 a).Since 5000 a,the interval between strong earthquake recurrences gradually decreases,indicating an increasing risk of strong earthquakes along the Litang fault.This study presents a continuous record of paleo-earthquakes along the Litang fault,eastern Tibetan Plateau,and can enhance the understanding of regional seismic activity.展开更多
The Almus Fault Zone(AFZ)is one of the major splay faults of the North Anatolian Fault Zone(NAFZ)and is important for understanding its tectonic features and assessing regional seismic hazards.This research presents t...The Almus Fault Zone(AFZ)is one of the major splay faults of the North Anatolian Fault Zone(NAFZ)and is important for understanding its tectonic features and assessing regional seismic hazards.This research presents the integration of morphometric indices to quantitatively assess the spatial variation of tectonic activity along the AFZ.The AFZ is an active fault with both strike-slip and normal fault components and consists of two main branches,Mercimekdağı-Çamdere Fault(MÇF)and Tokat Fault(TF)segments.This study aims to assess the relative tectonic activity of the AFZ using various morphometric indices,based on a 10 m resolution DEM,with the aid of ArcGIS and MATLAB software.For this purpose,morphometric indices such as hypsometric integral(HI:0.35-0.65),mountain front sinuosity(Smf:1.3-1.44),valley floor width-height ratio(Vf:0.15-2.28),asymmetry factor(AF:23-77),drainage basin shape(Bs:1.13-6.10)and normalized steepness index(ksn:1-498)were applied to 53 drainage basins.When the Smf and mean Vf indices results were evaluated,it was calculated that the uplift ratio of the region was more than 0.5 mm/yr.The spatial distribution of the relative tectonic activity(Iat)of the area was revealed by combining the obtained morphometric indices analysis results.According to the Iat result,it was concluded that the MercimekdağıÇamdere Fault and Tokat Fault segments have high tectonic activity,but the Mercimekdağı-Çamdere Fault segment has higher tectonic activity.The results obtained were also confirmed by field observations.This research provides valuable information for the evaluation of tectonic activity in drainage systems controlled by splay faults.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
While injection-induced seismicity has been widely studied,its implications for CO_(2)geological storage require reevaluation due to distinct fluid-rock interactions.This study develops a coupled hydromechanical model...While injection-induced seismicity has been widely studied,its implications for CO_(2)geological storage require reevaluation due to distinct fluid-rock interactions.This study develops a coupled hydromechanical model incorporating rate-and-state friction laws to investigate fault reactivation mechanisms during early-stage CO_(2)injection.The competing effects of pore pressure diffusion and fluid pressurization are systematically investigated,considering three key factors:permeability variations within fault damage zones,normal stress variation coefficients,and injection parameters.Numerical simulations reveal that slower CO_(2)migration causes limited pressure perturbation(<0.3 MPa over 15 d)compared to single-phase fluid injection.Fluid pressurization enhances fault strength and delays reactivation,though this stabilizing effect diminishes in low-permeability damage zones.Highly permeable damage zones promote larger rupture areas despite strengthening from pressurization,as reduced effective stress accelerates failure.Paradoxically,while fluid pressurization increases fault strength,it simultaneously elevates seismic risk through amplified stress drops during slip events.Temporal analysis shows that fluid pressurization dominates initial fault response,while sustained pore pressure diffusion ultimately drives reactivation.Increased normal stress variation coefficients and injection rates accelerate localized rupture initiation but restrict propagation due to non-critically stressed states.This discrepancy demonstrates that regions with positive Coulomb failure stress changes do not correlate well with actual slip zones.These findings highlight the critical interplay between transient pressurization effects and progressive pressure diffusion during early CO_(2)injection phases,providing crucial insights for seismic risk management in CO_(2)storage projects.展开更多
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
Strong seismic excitation and fault dislocation are likely to occur simultaneously in high-intensity seismic zones,causing severe damage to tunnels crossing active fault zones.This paper aims to develop a novel analyt...Strong seismic excitation and fault dislocation are likely to occur simultaneously in high-intensity seismic zones,causing severe damage to tunnels crossing active fault zones.This paper aims to develop a novel analytical solution to determine the longitudinal mechanical responses of tunnels subjected to the combined effects of seismic waves and strike-slip faulting.Adopting the elastic springbeam model,the seismic waves are modelled as shear horizontal(SH)waves and the fault dislocation follows an S-shaped pattern;the superposition principle for free-fielddisplacements caused by both effects is assumed.In addition,the transmission and reflectionof seismic waves at the fault-rock geological interface and the tangential contact conditions at the tunnel-rock interface are considered.The analytical model is validated against numerical simulations,confirmingits accuracy in calculating tunnel responses.Moreover,a parametric study is conducted to evaluate the impact of key factors,including fault displacement,fault zone width,fault dip angle,earthquake frequency,rock conditions,tunnel lining stiffness,and tangential contact conditions,on tunnel responses.Compared with each effect alone,the combined effects of seismic waves and strike-slip faulting significantlychange the tunnel deformation and internal forces,leading to increased tunnel responses,especially within the fault zone and near the fault-rock interfaces.Depending on specificparameters,tunnel responses can be classifiedinto seismic-dominated,faulting-dominated,and seismic-faulting coupled responses on the basis of the relative contributions of each effect.The proposed analytical solution can be applied to quickly predict the longitudinal mechanical behaviour of tunnels under such combined effects in engineering applications.展开更多
Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an uns...Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.展开更多
Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.Ho...Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features.展开更多
Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault char...Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.展开更多
基金financially supported by the National Key Research and Development Program of China(No.2021YFC3100700)the National Natural Science Foundation of China(No.42376070)+1 种基金the Natural Science Foundation of Guangdong Province(No.2024A1515012371)the Rising Star Foundation of the South China Sea Institute of Oceanology(No.NHXX2019DZ0201)。
文摘Polygonal fault systems(PFS),characterized by multi-directional fault patterns within layered sequences,are well-documented features in global continental margin basins.While the geometry and formation mechanisms of PFS have been extensively studied in the northern South China Sea,the PFS in the Zhongjiannan Basin(western South China Sea)remain relatively unexplored,with a lack of quantitative analysis regarding their propagation.This study addresses this gap by using high-resolution three-dimensional(3D)seismic data and conducting a quantitative fault analysis to thoroughly examine the planform,cross-sectional geometry,and evolution of PFS in the northern Zhongjiannan Basin.The absence of a dominant strike direction among these polygonal faults suggests that their evolution is not controlled by anisotropic stress.Our interpretation of seismic data,constrained by the spatial relationship among PFS,gullies,and pockmarks,indicates that PFS mainly developed within the Miocene strata,with their initiation occurring during the late Miocene.Furthermore,the PFS act as key conduits connecting gullies to pockmarks in this area.The formation and development of PFS may be primarily driven by thermally triggered processes within siliceous sediments.The necessary heat source is probably associated with the abundant submarine magmatism observed in the Zhongjiannan Basin.To reconstruct the regional geological history,a four-stage evolutionary model,incorporating the formation of PFS,is presented.This research significantly improves our understanding of the regional geological evolution of the Zhongjiannan Basin,providing critical insights into the initiation and development of PFS in the western South China Sea.
基金supported by the National Natural Science Foundation of China(42230312,42272270,42172262,42372266)the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(2024ZD1000500)the China Geological Survey Project(DD20240041).
文摘The Guanxian-Anxian fault zone in the Longmen Shan,Sichuan,China,exhibits long-term creep-slip but ruptured during the 2008 Wenchuan earthquake,challenging the view that creeping faults rarely generate strong earthquakes.To investigate the transition from creep-slip to stick-slip,we analyzed fault rocks from the WFSD-3,using microstructural observations,XRD,μXRF,Raman spectroscopy,and quartz grain size statistics.Fault rocks show intense foliation,pressure-solution structures,and abundant clay minerals,reflecting long-term aseismic creep.At the interface between black and gray fault gouges at~1249.98 m,microstructures indicate stick-slip behavior,including truncated grains,angular fragments,and finer grain sizes.Here,clay content drops sharply while strong minerals(quartz,feldspar,calcite,dolomite)increase.Elemental mapping shows Al and K enriched in black gouge,whereas Ca and Si in gray gouge;Raman spectroscopy indicates possible graphitization;the finest quartz grains occur in black gouge.These features mark co-seismic principal slip zone of the Wenchuan earthquake.We propose that fluid-driven transformation of strong minerals into clays facilitates creep-slip,whereas localized precipitation of strong minerals strengthens the fault,causing stress accumulation and controlling the creep-slip to stick-slip transition.This mechanism has implications for reassessing seismic hazards of creeping faults.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130101,42474002,42374003&42564002)the Jiangxi Provincial Natural Science Foundation(Grant No.20252BAC240262).
文摘Accurate fault modeling is essential for understanding earthquake rupture processes and improving seismic hazard assessment.We present a unified framework that integrates geodetic data with multidisciplinary constraints,including relocated aftershocks,geological observations,and geophysical information,to adaptively model fault geometry and slip in diverse scenarios such as multi-segment and multi-event ruptures.The framework combines adaptive fault construction with a scenario-driven Bayesian joint inversion approach.Fault geometries are first built from prior constraints,such as surface ruptures and aftershocks,and then refined through probabilistic inference when such data are inadequate.To enhance computational efficiency,we introduce a Sequential Monte Carlo Fukuda-Johnson(SMC-FJ)strategy.This separates nonlinear parameters-including geometry,data weights,and smoothing factors-from linear slip parameters,which are conditionally solved via constrained least squares.Geometry updates follow a hierarchical adjustment scheme based on point,line,and structural units,enabling flexibility across rupture complexities.Synthetic tests and four case studies,including the 2022 Menyuan,2023 Türkiye,2022 Luding,and 2019 Ridgecrest earthquakes,demonstrate robustness under various constraints.For the Menyuan earthquake,full Bayesian inversion confirms that the fault geometry constrained by relocated aftershocks is highly accurate and needs only minor adjustment to match the observed surface deformation.The results further show that gradual changes in fault strike and dip modulated rupture arrest and postseismic stress accumulation,highlighting the critical role of inherited geometric structure in controlling rupture termination and delayed seismic activation.
基金funded by the Science and Technology Projects of State Grid Zhejiang Electric Power Co.,Ltd.(5211DS24000G).
文摘With the rapid development of large-scale offshore wind farms,efficient and reliable power transmission systems are urgently needed.Hybrid high-voltage direct current(HVDC)configurations combining a diode rectifier unit(DRU)and a modular multilevel converter(MMC)have emerged as a promising solution,offering advantages in cost-effectiveness and control capability.However,the uncontrollable nature of the DRU poses significant challenges for systemstability under offshore AC fault conditions,particularly due to its inability to provide fault current or voltage support.This paper investigates the offshore AC fault characteristics and fault ride-through(FRT)strategy of a hybrid offshore wind power transmission system based on a diode rectifier unit DRU and MMC.First,the dynamic response of the hybrid system under offshore symmetrical three-phase faults is analyzed.It is demonstrated that due to the unidirectional conduction nature of the DRU,its AC current rapidly drops to zero during faults,and the fault current is solely contributed by the wind turbine generators(WTGs)and wind farm MMC(WFMMC).Based on this analysis,a coordinated FRT strategy is proposed,which combines a segmented current limiting control for the wind-turbine(WT)grid-side converters(GSCs)and a constant AC current control for the WFMMC.The strategy ensures effective voltage support during the fault and prevents MMC current saturation during fault recovery,enabling fast and stable system restoration.Electromagnetic transient simulations in PSCAD/EMTDC verify the feasibility of the proposed fault ride-through strategy.
基金supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(2024ZD1000500)the National Natural Science Foundation of China(42172262 and 42372266)+1 种基金the China Geological Survey(DD20240041)the Fundamental Research Funds of the Institute of Geomechanics(DZLXJK202516).
文摘The Yingxiu-Beichuan fault zone(YBFZ)has long been active and experienced repeated large earthquakes.The physicochemical properties of the deep fault zone(>1000 m)are the key to understanding the deformation mechanism of large earthquakes.This study uses rock magnetic,microstructural,and geochemical analyses of representative samples exposed in FZ1681 within the Wenchuan Earthquake Fault Scientific Drilling borehole 2(WFSD-2)cores.Fault gouge and fault breccia have higher magnetic susceptibility values than wall rocks,and they contain abundant paramagnetic minerals and small quantities of magnetite and monoclinic pyrrhotite.The magnetite and monoclinic pyrrhotite in the fault gouge were mainly formed by coseismic frictional heating,indicating that large earthquakes with frictional heating temperatures of~500-900℃once occurred in the YBFZ.The seismogenic and coseismic environment was reducing with a relatively high sulfur content.The monoclinic pyrrhotite in the fault breccia was formed mainly by low-temperature hydrothermal fluid.This indicates that the fault zone experienced reducing and low-temperature(<400℃)hydrothermal fluid with a relatively high sulfur content after the earthquake.The YBFZ,which experiences frequent large earthquakes,is weakly oxidizing environment at different depths,but the effect of the low-temperature hydrothermal fluid is weaker at depth.
文摘Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)control strategy based on a power outer loop and voltage-current inner loops,aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions.During voltage sags,the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support,thereby effectively suppressing the steady-state component of the fault current.To address the active power imbalance induced by voltage sags,a dynamic active power reference correction method based on apparent power is designed to mitigate power angle oscillations and limit transient current.Moreover,an adaptive virtual impedance loop is implemented to enhance dynamic transient current-limiting performance during the fault initiation phase.This approach improves the responsiveness of the inner loop and ensures safe system operation under various fault severities.Under asymmetric fault conditions,a negative-sequence reactive current compensation strategy is incorporated to further suppress negative-sequence voltage and improve voltage symmetry.The proposed control scheme enables coordinated operation of multiple control objectives,including voltage support,current suppression,and power angle stability,across different fault scenarios.Finally,MATLAB/Simulink simulation results validate the effectiveness of the proposed strategy,showcasing its superior performance in current limiting and power angle stability,thereby significantly enhancing the system’s fault ride-through capability.
基金support from the Scientific Funding for the Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ354)。
文摘Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
基金funded by the Science and Technology Vice President Project in Changping District,Beijing(Project Name:Research on multi-scale optimization and intelligent control technology of integrated energy systemProject number:202302007013).
文摘The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
基金supported by the National Natural Science Foundation of China(42202131 and 42177184).
文摘This study examines a 1.32 m thick sediment sequence from the Cunge sag pond in the Litang Basin,eastern Tibetan Plateau,to assess the seismicity of the Litang fault during the Holocene.High-resolution geochemical,grain size,magnetic susceptibility,and total organic carbon indicators are employed to obtain a continuous record of changes in elemental,physical,and biological properties within the profile to identify seismic events.The seismic event layer generally comprises two sedimentary rhythms:a lower coarse sand layer and an upper fine silt-clay layer.These layers represent rapid deposition associated with fault activity(Earthquake A)and slower deposition during calm periods or earthquake recurrence intervals(Seismic interval A).Through six^(14)C dating,five seismic events have been identified in the Cunge sag pond section:E1(before 3955 a B.P.),E2(3713-3703 a B.P.),E3(3492-3392 a B.P.),E4(2031-1894 a B.P.),and E5(1384-1321 a B.P.).E1-E4 had shown a good consistency with the paleo-earthquake recorded by the trench,and whereas E5 is a newly identified seismic event,further improving the continuous earthquake sequence of the Litang fault.Based on existing trench data and the seismic event record from the Cunge sag pond,a total of 11 paleo-earthquakes are identified along the Litang fault since the Holocene.The paleo-earthquake activity of the Litang fault exhibits a clustered pattern,with recurrence intervals of both long periods(1000 a)and short periods(500 a).Since 5000 a,the interval between strong earthquake recurrences gradually decreases,indicating an increasing risk of strong earthquakes along the Litang fault.This study presents a continuous record of paleo-earthquakes along the Litang fault,eastern Tibetan Plateau,and can enhance the understanding of regional seismic activity.
文摘The Almus Fault Zone(AFZ)is one of the major splay faults of the North Anatolian Fault Zone(NAFZ)and is important for understanding its tectonic features and assessing regional seismic hazards.This research presents the integration of morphometric indices to quantitatively assess the spatial variation of tectonic activity along the AFZ.The AFZ is an active fault with both strike-slip and normal fault components and consists of two main branches,Mercimekdağı-Çamdere Fault(MÇF)and Tokat Fault(TF)segments.This study aims to assess the relative tectonic activity of the AFZ using various morphometric indices,based on a 10 m resolution DEM,with the aid of ArcGIS and MATLAB software.For this purpose,morphometric indices such as hypsometric integral(HI:0.35-0.65),mountain front sinuosity(Smf:1.3-1.44),valley floor width-height ratio(Vf:0.15-2.28),asymmetry factor(AF:23-77),drainage basin shape(Bs:1.13-6.10)and normalized steepness index(ksn:1-498)were applied to 53 drainage basins.When the Smf and mean Vf indices results were evaluated,it was calculated that the uplift ratio of the region was more than 0.5 mm/yr.The spatial distribution of the relative tectonic activity(Iat)of the area was revealed by combining the obtained morphometric indices analysis results.According to the Iat result,it was concluded that the MercimekdağıÇamdere Fault and Tokat Fault segments have high tectonic activity,but the Mercimekdağı-Çamdere Fault segment has higher tectonic activity.The results obtained were also confirmed by field observations.This research provides valuable information for the evaluation of tectonic activity in drainage systems controlled by splay faults.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金funded by Joint Funds of the National Natural Science Foundation of China(Grant No.U23A20671)the Major Project of Inner Mongolia Science and Technology(Grant No.2021ZD0034)the Creative Groups of Natural Science Foundation of Hubei Province,China(Grant No.2021CFA030).
文摘While injection-induced seismicity has been widely studied,its implications for CO_(2)geological storage require reevaluation due to distinct fluid-rock interactions.This study develops a coupled hydromechanical model incorporating rate-and-state friction laws to investigate fault reactivation mechanisms during early-stage CO_(2)injection.The competing effects of pore pressure diffusion and fluid pressurization are systematically investigated,considering three key factors:permeability variations within fault damage zones,normal stress variation coefficients,and injection parameters.Numerical simulations reveal that slower CO_(2)migration causes limited pressure perturbation(<0.3 MPa over 15 d)compared to single-phase fluid injection.Fluid pressurization enhances fault strength and delays reactivation,though this stabilizing effect diminishes in low-permeability damage zones.Highly permeable damage zones promote larger rupture areas despite strengthening from pressurization,as reduced effective stress accelerates failure.Paradoxically,while fluid pressurization increases fault strength,it simultaneously elevates seismic risk through amplified stress drops during slip events.Temporal analysis shows that fluid pressurization dominates initial fault response,while sustained pore pressure diffusion ultimately drives reactivation.Increased normal stress variation coefficients and injection rates accelerate localized rupture initiation but restrict propagation due to non-critically stressed states.This discrepancy demonstrates that regions with positive Coulomb failure stress changes do not correlate well with actual slip zones.These findings highlight the critical interplay between transient pressurization effects and progressive pressure diffusion during early CO_(2)injection phases,providing crucial insights for seismic risk management in CO_(2)storage projects.
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
基金supported by the National Natural Science Foundation of China(No.41941018)Shanghai Gaofeng Discipline Construction Funding.
文摘Strong seismic excitation and fault dislocation are likely to occur simultaneously in high-intensity seismic zones,causing severe damage to tunnels crossing active fault zones.This paper aims to develop a novel analytical solution to determine the longitudinal mechanical responses of tunnels subjected to the combined effects of seismic waves and strike-slip faulting.Adopting the elastic springbeam model,the seismic waves are modelled as shear horizontal(SH)waves and the fault dislocation follows an S-shaped pattern;the superposition principle for free-fielddisplacements caused by both effects is assumed.In addition,the transmission and reflectionof seismic waves at the fault-rock geological interface and the tangential contact conditions at the tunnel-rock interface are considered.The analytical model is validated against numerical simulations,confirmingits accuracy in calculating tunnel responses.Moreover,a parametric study is conducted to evaluate the impact of key factors,including fault displacement,fault zone width,fault dip angle,earthquake frequency,rock conditions,tunnel lining stiffness,and tangential contact conditions,on tunnel responses.Compared with each effect alone,the combined effects of seismic waves and strike-slip faulting significantlychange the tunnel deformation and internal forces,leading to increased tunnel responses,especially within the fault zone and near the fault-rock interfaces.Depending on specificparameters,tunnel responses can be classifiedinto seismic-dominated,faulting-dominated,and seismic-faulting coupled responses on the basis of the relative contributions of each effect.The proposed analytical solution can be applied to quickly predict the longitudinal mechanical behaviour of tunnels under such combined effects in engineering applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375255,51935007)the Shanghai Rising-Star Program(Grant No.24QB2705000)。
文摘Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.
基金supported by the National Science Foundationof China(Nos.52305127 and 52475130)。
文摘Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features.
基金supported by the National Natural Science Foundation of China (Grant Nos.62125301,62021003,62303024,U24A20275,62522302,62473011,92467205)the National Key Research and Development Project (Grant Nos.2022YFB3305800-5,2024YFE0212400)+2 种基金the Youth Beijing Scholars Program (Grant No.037)the Beijing Nova Program (Grant Nos.20240484694,20250484938)the Beijing Natural Science Foundation (Grant No.L253010)。
文摘Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.
