Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set inco...The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model,enabling effective identification of small-scale strike-slip faults through seismic data interpretation.Based on the CNN faults,we analyze the distribution patterns of small-scale strike-slip faults.The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m.The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member.The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics,separated by a low-brittleness layer.The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member,while the low-brittleness layer exerts restrictive effects on vertical fault propagation.Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults.All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults,particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.展开更多
The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly impro...The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards.Utilising the SKUA GoCAD software,we constructed detailed seismic fault models for the 2021 M_(S)6.4 Yangbi earthquake in Yunnan,China,using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow.Our analysis revealed a NW-striking main fault with a high-angle SW dip,accompanied by two branch faults.Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault,whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern.Additionally,a third ENE-striking short fault was identified NE of the main fault.In combination with the spatial distribution of pre-existing faults,our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW-and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone.The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones,through either cascade or conjugate rupture modes,can cause unexpected moderate-large earthquakes and severe disasters,necessitating attention in regions like southeast Xizang,which have complex fault systems.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
Superconducting radio-frequency(SRF)cavities are the core components of SRF linear accelerators,making their stable operation considerably important.However,the operational experience from different accelerator labora...Superconducting radio-frequency(SRF)cavities are the core components of SRF linear accelerators,making their stable operation considerably important.However,the operational experience from different accelerator laboratories has revealed that SRF faults are the leading cause of short machine downtime trips.When a cavity fault occurs,system experts analyze the time-series data recorded by low-level RF systems and identify the fault type.However,this requires expertise and intuition,posing a major challenge for control-room operators.Here,we propose an expert feature-based machine learning model for automating SRF cavity fault recognition.The main challenge in converting the"expert reasoning"process for SRF faults into a"model inference"process lies in feature extraction,which is attributed to the associated multidimensional and complex time-series waveforms.Existing autoregression-based feature-extraction methods require the signal to be stable and autocorrelated,resulting in difficulty in capturing the abrupt features that exist in several SRF failure patterns.To address these issues,we introduce expertise into the classification model through reasonable feature engineering.We demonstrate the feasibility of this method using the SRF cavity of the China accelerator facility for superheavy elements(CAFE2).Although specific faults in SRF cavities may vary across different accelerators,similarities exist in the RF signals.Therefore,this study provides valuable guidance for fault analysis of the entire SRF community.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
Knowledge of the seismogenic environment of fault zones is critical for understanding the processes and mechanisms of large earthquakes.We conducted a rock magnetic study of the fault rocks and protoliths to investiga...Knowledge of the seismogenic environment of fault zones is critical for understanding the processes and mechanisms of large earthquakes.We conducted a rock magnetic study of the fault rocks and protoliths to investigate the seismogenic environment of earthquakes in the Motuo fault zone,in the eastern Himalayan syntaxis.The results indicate that magnetite is the principal magnetic carrier in the fault rocks and protolith,while the protolith has a higher content of paramagnetic minerals than the fault rocks.The fault rocks are characterized by a high magnetic susceptibility relative to the protolith in the Motuo fault zone.This is likely due to the thermal alteration of paramagnetic minerals to magnetite caused by coseismic frictional heating with concomitant hydrothermal fluid circulation.The high magnetic susceptibility of the fault rocks and neoformed magnetite indicate that large earthquakes with frictional heating temperatures>500℃have occurred in the Motuo fault zone in the past,and that the fault maintained an oxidizing environment with weak fluid action during these earthquakes.Our results reveal the seismogenic environment of the Motuo fault zone,and they are potentially important for the evaluation of the regional stability in the eastern Himalayan syntaxis.展开更多
A complex geological environment with faults can be encountered in the process of coal mining.Fault activation can cause instantaneous structure slipping,releasing a significant amount of elastic strain energy during ...A complex geological environment with faults can be encountered in the process of coal mining.Fault activation can cause instantaneous structure slipping,releasing a significant amount of elastic strain energy during underground coal mining.This would trigger strong rockburst disasters.To understand the occurrence of fault-slip induced rockbursts,we developed a physical model test system for fault-slip induced rockbursts in coal mine drifts.The boundary energy storage(BES)loading apparatus and bottom rapid retraction(BRR)apparatus are designed to realize energy compensation and continuous boundary stress transfer of the surrounding rocks for instantaneous fault slip,as well as to provide space for the potential fault slip.Taking the typical fault-slip induced rockburst in the Xinjulong Coal Mine,China,as the background,we conducted a model test using the test system.The deformation and stress in the rock surrounding the drift and the support unit force during fault slip are analyzed.The deformation and failure characteristics and dynamic responses of drifts under fault-slip induced rockbursts are obtained.The test results illustrate the rationality and effectiveness of the test system.Finally,corresponding recommendations and prospects are proposed based on our findings.展开更多
To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precis...To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.展开更多
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structur...The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structure in the upper crust and seismogenic tectonics in the Hefei segment of this fault, we collected phase velocity dispersion data of fundamental mode Rayleigh waves from ambient noise cross-correlation functions of ~400 temporal seismographs in an area of approximately 80 × 70 km along the fault zone. The period band of the dispersion data was ~0.5–10 s. We inverted for the upper crustal three-dimensional(3-D) shear velocity model with azimuthal anisotropy from the surface to 10 km depth by using a 3-D direct azimuthal anisotropy inversion method. The inversion result shows the spatial distribution characteristics of the tectonic units in the upper crust. Additionally, the deformation of the Tan-Lu Fault Zone and its conjugated fault systems could be inferred from the anisotropy model. In particular, the faults that have remained active from the early and middle Pleistocene control the anisotropic characteristics of the upper crustal structure in this area. The direction of fast axes near the fault zone area in the upper crust is consistent with the strike of the faults, whereas for the region far away from the fault zone, the direction of fast axes is consistent with the direction of the regional principal stress caused by plate movement. Combined with the azimuthal anisotropy models in the deep crust and uppermost mantle from the surface wave and Pn wave, the different anisotropic patterns caused by the Tan-Lu Fault Zone and its conjugated fault system nearby are shown in the upper and lower crust. Furthermore,by using the double-difference method, we relocated the Lujiang earthquake series, which contained 32 earthquakes with a depth shallower than 10 km. Both the Vs model and earthquake relocation results indicate that earthquakes mostly occurred in the vicinity of structural boundaries with fractured media, with high-level development of cracks and small-scale faults jammed between more rigid areas.展开更多
As the mine depth around the world increases,the temperature of the surrounding rock of the mining workface increases significantly.To control the heat hazards,the hot water in the mining floor is developed during min...As the mine depth around the world increases,the temperature of the surrounding rock of the mining workface increases significantly.To control the heat hazards,the hot water in the mining floor is developed during mining to decrease the min-ing workface temperature while also developing geothermal energy.This method is called the co-exploitation of mine and geothermal energy(CMGE).The geothermal development may precipitate the large-scale failure of the nearby fault zone during the mining process.However,the evolution of shear slide and shear failure of fault under geothermal production/rein-jection during mining is missing.Therefore,a fully-coupled hydraulic mechanism(HM)double-medium model for CMGE was developed based on the measured data of the Chensilou mine.A comparative analysis of the mechanical response of fault between CMGE and single mining was conducted.The disturbance of geothermal production pressure and reinjection pressure under mining on fault stability were respectively expounded.The results indicate that:(1)The disturbance of geo-thermal reinjection amplifies the disturbance of mining on fault stability.The amplified effect resulted in a normal stress drop of the fault,further leading to a substantial increase in shear slide distance,failure area,and cumulative seismic moment of fault compared with the single mining process.(2)As the distance of reinjection well to the fault decreases,the fault failure intensity increases.Setting the production well within the fault is advantageous for controlling fault stability under CMGE.(3)The essence of the combined disturbance of CMGE on the nearby fault is the overlay of tensile stress disturbance on the fault rock mass of the mining and geothermal reinjection.Though the geothermal reinjection causes a minor normal stress drop of fault,it can result in a more serious fault failure under CMGE.This paper supplies a significant gap in understanding thenearby faults failure under CMGE.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow ...The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.展开更多
An in-memory storage system provides submillisecond latency and improves the concurrency of user applications by caching data into memory from external storage.Fault tolerance of in-memory storage systems is essential...An in-memory storage system provides submillisecond latency and improves the concurrency of user applications by caching data into memory from external storage.Fault tolerance of in-memory storage systems is essential,as the loss of cached data requires access to data from external storage,which evidently increases the response latency.Typically,replication and erasure code(EC)are two fault-tolerant schemes that pose different trade-offs between access performance and storage usage.To help make the best performance and space trade-off,we design ElasticMem,a hybrid fault-tolerant distributed in-memory storage system that supports elastic redundancy transition to dynamically change the fault-tolerant scheme.ElasticMem exploits a novel EC-oriented replication(EOR)that carefully designs the data placement of replication according to the future data layout of EC to enhance the I/O efficiency of redundancy transition.ElasticMem solves the consistency problem caused by concurrent data accesses via a lightweight table-based scheme combined with data bypassing.It detects correlated read and write requests and serves subsequent read requests with local data.We implement a prototype that realizes ElasticMem based on Memcached.Experiments show that ElasticMem remarkably reduces the time of redundancy transition,the overall latency of correlated concurrent data accesses,and the latency of single data access among them.展开更多
Taking the Wangfu fault depression in the Songliao Basin as an example,on the basis of seismic interpretation and drilling data analysis,the distribution of the basement faults was clarified,the fault activity periods...Taking the Wangfu fault depression in the Songliao Basin as an example,on the basis of seismic interpretation and drilling data analysis,the distribution of the basement faults was clarified,the fault activity periods of the coal-bearing formations were determined,and the fault systems were divided.Combined with the coal seam thickness and actual gas indication in logging,the controls of fault systems in the rift basin on the spatial distribution of coal and the occurrence of coal-rock gas were identified.The results show that the Wangfu fault depression is an asymmetrical graben formed under the control of basement reactivated strike-slip T-rupture,and contains coal-bearing formations and five sub-types of fault systems under three types.The horizontal extension strength,vertical activity strength and tectono-sedimentary filling difference of basement faults control vertical stratigraphic sequences,accumulation intensity,and accumulation frequency of coal seam in rift basin.The structural transfer zone formed during the segmented reactivation and growth of the basement faults controls the injection location of steep slope exogenous clasts.The filling effect induced by igneous intrusion accelerates the sediment filling process in the rift lacustrine area.The structural transfer zone and igneous intrusion together determine the preferential accumulation location of coal seams in the plane.The faults reactivated at the basement and newly formed during the rifting phase serve as pathways connecting to the gas source,affecting the enrichment degree of coal-rock gas.The vertical sealing of the faults was evaluated by using shale smear factor(SSF),and the evaluation criterion was established.It is indicated that the SSF is below 1.1 in major coal areas,indicating favorable preservation conditions for coal-rock gas.Based on the influence factors such as fault activity,segmentation and sealing,the coal-rock gas accumulation model of rift basin was established.展开更多
Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper...Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper maintenance of survey meters are important in order to ascertain their accuracy and reliability. This study provides a comprehensive retrospective assessment of the calibration behaviour, durability, and fault trends of 160 survey meters, spanning ten different models. They were calibrated at the Secondary Standard Dosimetry Laboratory (SSDL) in Nigeria over a decade (2012-2023) using an X-Ray Beam Irradiator Model X80-225K and Cs-137 irradiator (OB6) with a PTW reference spherical chamber traceable to the IAEA SSDL in Seibersdorf, Austria. The calibration stability of each model was evaluated, revealing that models like Instrument A and Instrument B demonstrated high reliability with calibration factors close to the ideal value of 1, while models like Instrument C exhibited higher variability, suggesting less consistent performance for dose rate monitoring. Fault analysis showed that the most common issues were related to the battery compartment, indicating a need for improved handling practices. Correlation analysis reveals no statistically significant correlation between calibration factor and age of survey meter across the analysed models. The study concludes that regular calibration, proper handling, and user training are crucial for maintaining the accuracy and longevity of radiation detectors.展开更多
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Natural Science Foundation of China(No.U21B2062).
文摘The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model,enabling effective identification of small-scale strike-slip faults through seismic data interpretation.Based on the CNN faults,we analyze the distribution patterns of small-scale strike-slip faults.The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m.The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member.The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics,separated by a low-brittleness layer.The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member,while the low-brittleness layer exerts restrictive effects on vertical fault propagation.Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults.All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults,particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.
基金financial support from the National Key R&D Program of China (No. 2021YFC3000600)National Natural Science Foundation of China (No. 41872206)National Nonprofit Fundamental Research Grant of China, Institute of Geology, China, Earthquake Administration (No. IGCEA2010)
文摘The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards.Utilising the SKUA GoCAD software,we constructed detailed seismic fault models for the 2021 M_(S)6.4 Yangbi earthquake in Yunnan,China,using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow.Our analysis revealed a NW-striking main fault with a high-angle SW dip,accompanied by two branch faults.Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault,whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern.Additionally,a third ENE-striking short fault was identified NE of the main fault.In combination with the spatial distribution of pre-existing faults,our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW-and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone.The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones,through either cascade or conjugate rupture modes,can cause unexpected moderate-large earthquakes and severe disasters,necessitating attention in regions like southeast Xizang,which have complex fault systems.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
文摘Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
基金supported by the studies of intelligent LLRF control algorithms for superconducting RF cavities(No.E129851YR0)the National Natural Science Foundation of China(No.U22A20261)Applications of Artificial Intelligence in the Stability Study of Superconducting Linear Accelerators(No.E429851YR0)。
文摘Superconducting radio-frequency(SRF)cavities are the core components of SRF linear accelerators,making their stable operation considerably important.However,the operational experience from different accelerator laboratories has revealed that SRF faults are the leading cause of short machine downtime trips.When a cavity fault occurs,system experts analyze the time-series data recorded by low-level RF systems and identify the fault type.However,this requires expertise and intuition,posing a major challenge for control-room operators.Here,we propose an expert feature-based machine learning model for automating SRF cavity fault recognition.The main challenge in converting the"expert reasoning"process for SRF faults into a"model inference"process lies in feature extraction,which is attributed to the associated multidimensional and complex time-series waveforms.Existing autoregression-based feature-extraction methods require the signal to be stable and autocorrelated,resulting in difficulty in capturing the abrupt features that exist in several SRF failure patterns.To address these issues,we introduce expertise into the classification model through reasonable feature engineering.We demonstrate the feasibility of this method using the SRF cavity of the China accelerator facility for superheavy elements(CAFE2).Although specific faults in SRF cavities may vary across different accelerators,similarities exist in the RF signals.Therefore,this study provides valuable guidance for fault analysis of the entire SRF community.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
基金supported by the Fundamental Research Funds of the Institute of Geomechanics(DZLXJK202401)the National Natural Science Foundation of China(42177172,U2244226,42172255)+1 种基金the China Geological Survey Project(DD20230538)Deep Earth Probe and Mineral Resources ExplorationNational Science and Technology Major Project(2024ZD1000500)。
文摘Knowledge of the seismogenic environment of fault zones is critical for understanding the processes and mechanisms of large earthquakes.We conducted a rock magnetic study of the fault rocks and protoliths to investigate the seismogenic environment of earthquakes in the Motuo fault zone,in the eastern Himalayan syntaxis.The results indicate that magnetite is the principal magnetic carrier in the fault rocks and protolith,while the protolith has a higher content of paramagnetic minerals than the fault rocks.The fault rocks are characterized by a high magnetic susceptibility relative to the protolith in the Motuo fault zone.This is likely due to the thermal alteration of paramagnetic minerals to magnetite caused by coseismic frictional heating with concomitant hydrothermal fluid circulation.The high magnetic susceptibility of the fault rocks and neoformed magnetite indicate that large earthquakes with frictional heating temperatures>500℃have occurred in the Motuo fault zone in the past,and that the fault maintained an oxidizing environment with weak fluid action during these earthquakes.Our results reveal the seismogenic environment of the Motuo fault zone,and they are potentially important for the evaluation of the regional stability in the eastern Himalayan syntaxis.
基金support from the National Natural Science Foundation of China (Grant Nos.51927807,42077267 and 42277174).
文摘A complex geological environment with faults can be encountered in the process of coal mining.Fault activation can cause instantaneous structure slipping,releasing a significant amount of elastic strain energy during underground coal mining.This would trigger strong rockburst disasters.To understand the occurrence of fault-slip induced rockbursts,we developed a physical model test system for fault-slip induced rockbursts in coal mine drifts.The boundary energy storage(BES)loading apparatus and bottom rapid retraction(BRR)apparatus are designed to realize energy compensation and continuous boundary stress transfer of the surrounding rocks for instantaneous fault slip,as well as to provide space for the potential fault slip.Taking the typical fault-slip induced rockburst in the Xinjulong Coal Mine,China,as the background,we conducted a model test using the test system.The deformation and stress in the rock surrounding the drift and the support unit force during fault slip are analyzed.The deformation and failure characteristics and dynamic responses of drifts under fault-slip induced rockbursts are obtained.The test results illustrate the rationality and effectiveness of the test system.Finally,corresponding recommendations and prospects are proposed based on our findings.
基金Yongxian Huang supported by Projects of Guangzhou Science and Technology Plan(2023A04J0409)。
文摘To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
基金financially supported by the National Key Research and Development Program of China (2022YFC3005600)the Foundation of the Anhui Educational Commission (2023AH051198)+1 种基金the National Natural Science Foundation of China (42125401 and 42104063)the Joint Open Fund of Mengcheng National Geophysical Observatory (MENGO-202201)。
文摘The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structure in the upper crust and seismogenic tectonics in the Hefei segment of this fault, we collected phase velocity dispersion data of fundamental mode Rayleigh waves from ambient noise cross-correlation functions of ~400 temporal seismographs in an area of approximately 80 × 70 km along the fault zone. The period band of the dispersion data was ~0.5–10 s. We inverted for the upper crustal three-dimensional(3-D) shear velocity model with azimuthal anisotropy from the surface to 10 km depth by using a 3-D direct azimuthal anisotropy inversion method. The inversion result shows the spatial distribution characteristics of the tectonic units in the upper crust. Additionally, the deformation of the Tan-Lu Fault Zone and its conjugated fault systems could be inferred from the anisotropy model. In particular, the faults that have remained active from the early and middle Pleistocene control the anisotropic characteristics of the upper crustal structure in this area. The direction of fast axes near the fault zone area in the upper crust is consistent with the strike of the faults, whereas for the region far away from the fault zone, the direction of fast axes is consistent with the direction of the regional principal stress caused by plate movement. Combined with the azimuthal anisotropy models in the deep crust and uppermost mantle from the surface wave and Pn wave, the different anisotropic patterns caused by the Tan-Lu Fault Zone and its conjugated fault system nearby are shown in the upper and lower crust. Furthermore,by using the double-difference method, we relocated the Lujiang earthquake series, which contained 32 earthquakes with a depth shallower than 10 km. Both the Vs model and earthquake relocation results indicate that earthquakes mostly occurred in the vicinity of structural boundaries with fractured media, with high-level development of cracks and small-scale faults jammed between more rigid areas.
基金supported by the Key Project of the National Natural Science Foundation of China(U23B2091)the National Key R&D Program of China(2022YFC2905600)+1 种基金the Youth Project of the National Natural Science Foundation of China(52304104 and 52404157)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZB20240825).
文摘As the mine depth around the world increases,the temperature of the surrounding rock of the mining workface increases significantly.To control the heat hazards,the hot water in the mining floor is developed during mining to decrease the min-ing workface temperature while also developing geothermal energy.This method is called the co-exploitation of mine and geothermal energy(CMGE).The geothermal development may precipitate the large-scale failure of the nearby fault zone during the mining process.However,the evolution of shear slide and shear failure of fault under geothermal production/rein-jection during mining is missing.Therefore,a fully-coupled hydraulic mechanism(HM)double-medium model for CMGE was developed based on the measured data of the Chensilou mine.A comparative analysis of the mechanical response of fault between CMGE and single mining was conducted.The disturbance of geothermal production pressure and reinjection pressure under mining on fault stability were respectively expounded.The results indicate that:(1)The disturbance of geo-thermal reinjection amplifies the disturbance of mining on fault stability.The amplified effect resulted in a normal stress drop of the fault,further leading to a substantial increase in shear slide distance,failure area,and cumulative seismic moment of fault compared with the single mining process.(2)As the distance of reinjection well to the fault decreases,the fault failure intensity increases.Setting the production well within the fault is advantageous for controlling fault stability under CMGE.(3)The essence of the combined disturbance of CMGE on the nearby fault is the overlay of tensile stress disturbance on the fault rock mass of the mining and geothermal reinjection.Though the geothermal reinjection causes a minor normal stress drop of fault,it can result in a more serious fault failure under CMGE.This paper supplies a significant gap in understanding thenearby faults failure under CMGE.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金supported in part by Natural Science Foundation of Jiangsu Province under Grant BK20230255Natural Science Foundation of Shandong Province under Grant ZR2023QE281.
文摘The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.
基金supported by the Fundamental Research Funds for the Central Universities(WK2150110022)Anhui Provincial Natural Science Foundation(2208085QF189)National Natural Science Foundation of China(62202440).
文摘An in-memory storage system provides submillisecond latency and improves the concurrency of user applications by caching data into memory from external storage.Fault tolerance of in-memory storage systems is essential,as the loss of cached data requires access to data from external storage,which evidently increases the response latency.Typically,replication and erasure code(EC)are two fault-tolerant schemes that pose different trade-offs between access performance and storage usage.To help make the best performance and space trade-off,we design ElasticMem,a hybrid fault-tolerant distributed in-memory storage system that supports elastic redundancy transition to dynamically change the fault-tolerant scheme.ElasticMem exploits a novel EC-oriented replication(EOR)that carefully designs the data placement of replication according to the future data layout of EC to enhance the I/O efficiency of redundancy transition.ElasticMem solves the consistency problem caused by concurrent data accesses via a lightweight table-based scheme combined with data bypassing.It detects correlated read and write requests and serves subsequent read requests with local data.We implement a prototype that realizes ElasticMem based on Memcached.Experiments show that ElasticMem remarkably reduces the time of redundancy transition,the overall latency of correlated concurrent data accesses,and the latency of single data access among them.
基金Supported by the National Natural Science Foundation of China(42472190)Chongqing Natural Science Foundation Innovation and Development Joint Fund Project(CSTB2022NSCQ-LZX0020)Chongqing Talent Innovation and Entrepreneurship Leading Talent Project(0255-19230101042)。
文摘Taking the Wangfu fault depression in the Songliao Basin as an example,on the basis of seismic interpretation and drilling data analysis,the distribution of the basement faults was clarified,the fault activity periods of the coal-bearing formations were determined,and the fault systems were divided.Combined with the coal seam thickness and actual gas indication in logging,the controls of fault systems in the rift basin on the spatial distribution of coal and the occurrence of coal-rock gas were identified.The results show that the Wangfu fault depression is an asymmetrical graben formed under the control of basement reactivated strike-slip T-rupture,and contains coal-bearing formations and five sub-types of fault systems under three types.The horizontal extension strength,vertical activity strength and tectono-sedimentary filling difference of basement faults control vertical stratigraphic sequences,accumulation intensity,and accumulation frequency of coal seam in rift basin.The structural transfer zone formed during the segmented reactivation and growth of the basement faults controls the injection location of steep slope exogenous clasts.The filling effect induced by igneous intrusion accelerates the sediment filling process in the rift lacustrine area.The structural transfer zone and igneous intrusion together determine the preferential accumulation location of coal seams in the plane.The faults reactivated at the basement and newly formed during the rifting phase serve as pathways connecting to the gas source,affecting the enrichment degree of coal-rock gas.The vertical sealing of the faults was evaluated by using shale smear factor(SSF),and the evaluation criterion was established.It is indicated that the SSF is below 1.1 in major coal areas,indicating favorable preservation conditions for coal-rock gas.Based on the influence factors such as fault activity,segmentation and sealing,the coal-rock gas accumulation model of rift basin was established.
文摘Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper maintenance of survey meters are important in order to ascertain their accuracy and reliability. This study provides a comprehensive retrospective assessment of the calibration behaviour, durability, and fault trends of 160 survey meters, spanning ten different models. They were calibrated at the Secondary Standard Dosimetry Laboratory (SSDL) in Nigeria over a decade (2012-2023) using an X-Ray Beam Irradiator Model X80-225K and Cs-137 irradiator (OB6) with a PTW reference spherical chamber traceable to the IAEA SSDL in Seibersdorf, Austria. The calibration stability of each model was evaluated, revealing that models like Instrument A and Instrument B demonstrated high reliability with calibration factors close to the ideal value of 1, while models like Instrument C exhibited higher variability, suggesting less consistent performance for dose rate monitoring. Fault analysis showed that the most common issues were related to the battery compartment, indicating a need for improved handling practices. Correlation analysis reveals no statistically significant correlation between calibration factor and age of survey meter across the analysed models. The study concludes that regular calibration, proper handling, and user training are crucial for maintaining the accuracy and longevity of radiation detectors.
