A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show...The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.展开更多
The Longmenshan(LMS)fault zone is located at the junction of the eastern Tibetan Plateau and the Sichuan Basin and is of great significance for studying regional tectonics and earthquake hazards.Although regional velo...The Longmenshan(LMS)fault zone is located at the junction of the eastern Tibetan Plateau and the Sichuan Basin and is of great significance for studying regional tectonics and earthquake hazards.Although regional velocity models are available for the LMS fault zone,high-resolution velocity models are lacking.Therefore,a dense array of 240 short-period seismometers was deployed around the central segment of the LMS fault zone for approximately 30 days to monitor earthquakes and characterize fine structures of the fault zone.Considering the large quantity of observed seismic data,the data processing workflow consisted of deep learning-based automatic earthquake detection,phase arrival picking,and association.Compared with the earthquake catalog released by the China Earthquake Administration,many more earthquakes were detected by the dense array.Double-difference seismic tomography was adopted to determine V_(p),V_(s),and V_(p)/V_(s)models as well as earthquake locations.The checkerboard test showed that the velocity models have spatial resolutions of approximately 5 km in the horizontal directions and 2 km at depth.To the west of the Yingxiu–Beichuan Fault(YBF),the Precambrian Pengguan complex,where most of earthquakes occurred,is characterized by high velocity and low V_(p)/V_(s)values.In comparison,to the east of the YBF,the Upper Paleozoic to Jurassic sediments,where few earthquakes occurred,show low velocity and high V_(p)/V_(s)values.Our results suggest that the earthquake activity in the LMS fault zone is controlled by the strength of the rock compositions.When the high-resolution velocity models were combined with the relocated earthquakes,we were also able to delineate the fault geometry for different faults in the LMS fault zone.展开更多
The Ground-based Wide-Angle Cameras array necessitates the integration of more than 100 hardware devices,100 servers,and 2500 software modules that must be synchronized within a 3-second imaging cycle.However,the comp...The Ground-based Wide-Angle Cameras array necessitates the integration of more than 100 hardware devices,100 servers,and 2500 software modules that must be synchronized within a 3-second imaging cycle.However,the complexity of real-time,high-concurrency processing of large datasets has historically resulted in substantial failure rates,with an observation efficiency estimated at less than 50%in 2023.To mitigate these challenges,we developed a monitoring system designed to improve fault diagnosis efficiency.It includes two innovative monitoring views for“state evolution”and“transient lifecycle”.Combining these with“instantaneous state”and“key parameter”monitoring views,the system represents a comprehensive monitoring strategy.Here we detail the system architecture,data collection methods,and design philosophy of the monitoring views.During one year of fault diagnosis experimental practice,the proposed system demonstrated its ability to identify and localize faults within minutes,achieving fault localization nearly ten times faster than traditional methods.Additionally,the system design exhibited high generalizability,with possible applicability to other telescope array systems.展开更多
This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The ...This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.展开更多
This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-t...This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.展开更多
As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring an...As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.展开更多
The Lembang Fault is a major geological feature in West Java that borders the northern edge of Bandung(one of Indonesia’s largest cities).It lies just south of the active Tangkuban Perahu Volcano,exhibiting clear geo...The Lembang Fault is a major geological feature in West Java that borders the northern edge of Bandung(one of Indonesia’s largest cities).It lies just south of the active Tangkuban Perahu Volcano,exhibiting clear geomorphic signs of recent activity,and has been scientifically confirmed as active through geological and geophysical studies.In this work,we describe an Integrated along the Lembang Fault,which can be used for geodynamic research in Indonesia.We discuss the design of a seismic and Global Navigation Satellite System(GNSS)array sensor network for continuous monitoring,and report the status of monitoring stations that periodically collect highly accurate,continuous seismographic and GNSS readings,transmitting these data to a central server in Bandung for post-processing.Solutions from the array data are used to provide precise measurements of the deformation of the Earth’s surface over large distances,allowing for spatio-temporal tracking of tectonic movement,and resulting in a better understanding of seismic events in the region.In this study,our investigation revealed a significant compression rate of an estimated 13 microstrain/yr along the Lembang Fault,whereas the strain rate is much smaller farther south of the fault.This study presents the design of a seismo-geodetic observatory network that can be implemented in earthquake-prone regions for mitigation purposes,with particular utility for studying other active faults that also traverse populated areas in Indonesia.展开更多
Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study emplo...Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study employed a multi-local seismic monitoring network,integrating both in-mine and local instruments at overlapping length scales.We specifically focused on a damaging local magnitude(ML)2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province,China.Moment tensor(MT)inversion revealed a complex cascading rupture mechanism:an initial moment magnitude(M_(w))2.2 normal fault slip along the DF60 fault in an ESEeWNW direction,transitioning to a M_(w)3.0 event as the FD24 and DF60 faults unclamped.The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations,providing valuable insights into the driving mechanism of these seismic sequences.The in-mine network,constrained by its low dynamic changes,captured only the nucleation phase of the DF60 fault.Furthermore,standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture.To enhance safety and risk management in mining environments,we examined the implications of source reactivation within the cluster area post-stress-adjustment.This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity.The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.展开更多
Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonline...Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.展开更多
A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is：...A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.展开更多
The unique of using industrial LAN based on field bus to construct the system of vibration monitoring and fault diagnosis is introduced. The LAN topology, client/server architecture, database and designing of applicat...The unique of using industrial LAN based on field bus to construct the system of vibration monitoring and fault diagnosis is introduced. The LAN topology, client/server architecture, database and designing of application software for vibration monitoring and fault diagnosis are involved. How to apply industrial LAN to the vibration monitoring and fault diagnosis of turbo generator is discussed, and a scheme of how to construct the industrial LAN for vibration monitoring and fault diagnosis of turbo generator is presented.展开更多
Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com-...Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.展开更多
The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vib...The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vibration value of main sea water pump in the horizontal direction is abnormally high and malfunctions usually happen. Therefore, it is essential to make fault diagnosis of main sea water pump, By conventional off-line monitoring and vibration amplitude spectrum analysis, the fault cycle is found and the alarm value and stop value of equipment are set, which is helpful to equipment maintenance and accident prevention.展开更多
Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation ...Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation to continue in the presence of fault measurement.Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly.However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage.In this paper, the impact of leverage on the fault identification is analyzed.The leveraged RAIM fault identification（L-RAIM FI） method is proposed with consideration of the difference in leverage for each satellite in view.Furthermore,the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method.The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.展开更多
With the improvement of seismic observation system, more and more observations indicate that earthquakes may cause seismic velocity change. However, the amplitude and spatial distribution of the velocity variation rem...With the improvement of seismic observation system, more and more observations indicate that earthquakes may cause seismic velocity change. However, the amplitude and spatial distribution of the velocity variation remains a controversial issue. Recent active source monitoring carried out adjacent to Wenchuan Fault Scientific Drilling （WFSD） revealed unambiguous coseismic velocity change associated with a local M8 5.5 earthquake. Here, we carry out forward modeling using two-dimensional spectral element method to further investigate the amplitude and spatial distribution of observed velocity change. The model is well constrained by results from seismic reflection and WFSD coring. Our model strongly suggests that the observed coseismic velocity change is localized within the fault zone with width of ~ 120 m rather than dynamic strong ground shaking. And a velocity decrease of -2.0 % within the fault zone is required to fit the observed travel time delay distribution, which coincides with rock mechanical experiment and theoretical modeling.展开更多
Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of mo...Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of modern, high-end and key electromechanical equipment, this paper will describe the early faults prediction method for multi-type electromechanical systems, which is favorable for predicting early faults of complex electromechanical systems in non-stationary, nonlinear, variable working conditions and long-time running state; the paper shall introduce the reconfigurable integration technology of series safety monitoring systems based on which the integrated development platform of series safety monitoring systems is built. This platform can adapt to integrated R&D of series safety monitoring systems characterized by high technology, multiple species and low volume. With the help of this platform, series safety monitoring systems were developed, and the Remote Network Security Monitoring Center for Facility Groups was built. Experimental research and engineering applications show that: this new fault prediction method has realized the development trend features extraction of typical electromechanical systems, multi-information fusion, intelligent information decision-making and so on, improving the processing accuracy, relevance and applicability of information; new reconfigurable integration technologies have improved the integration level and R&D efficiency of series safety monitoring systems as well as expanded the scope of application; the series safety monitoring systems developed based on reconfigurable integration platform has already played an important role in many aspects including ensuring safety operation of equipment, stabilizing product quality, optimizing running state, saving energy consumption, reducing environmental pollution, improving working conditions, carrying out scientific maintenance, advancing equipment utilization, saving maintenance charge and enhancing the level of information management.展开更多
Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundan...Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system （RINS） in airborne navigation systems. An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertial navigation system （INS）. On this basis, the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS. The simulation is carried out based on the above algorithms, and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering. Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.展开更多
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
基金supported by the National Natural Science Foundation of China(NSFC 52175281,52475315)Youth Innovation Promotion Association of CAS(2021382)。
文摘The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.
基金supported by the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology under Grant 2024yjrc64the National Key R&D Program of China under Grant 2018YFC1504102。
文摘The Longmenshan(LMS)fault zone is located at the junction of the eastern Tibetan Plateau and the Sichuan Basin and is of great significance for studying regional tectonics and earthquake hazards.Although regional velocity models are available for the LMS fault zone,high-resolution velocity models are lacking.Therefore,a dense array of 240 short-period seismometers was deployed around the central segment of the LMS fault zone for approximately 30 days to monitor earthquakes and characterize fine structures of the fault zone.Considering the large quantity of observed seismic data,the data processing workflow consisted of deep learning-based automatic earthquake detection,phase arrival picking,and association.Compared with the earthquake catalog released by the China Earthquake Administration,many more earthquakes were detected by the dense array.Double-difference seismic tomography was adopted to determine V_(p),V_(s),and V_(p)/V_(s)models as well as earthquake locations.The checkerboard test showed that the velocity models have spatial resolutions of approximately 5 km in the horizontal directions and 2 km at depth.To the west of the Yingxiu–Beichuan Fault(YBF),the Precambrian Pengguan complex,where most of earthquakes occurred,is characterized by high velocity and low V_(p)/V_(s)values.In comparison,to the east of the YBF,the Upper Paleozoic to Jurassic sediments,where few earthquakes occurred,show low velocity and high V_(p)/V_(s)values.Our results suggest that the earthquake activity in the LMS fault zone is controlled by the strength of the rock compositions.When the high-resolution velocity models were combined with the relocated earthquakes,we were also able to delineate the fault geometry for different faults in the LMS fault zone.
基金supported by the Young Data Scientist Program of the China National Astronomical Data Center,the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550401)the National Natural Science Foundation of China(12494573).
文摘The Ground-based Wide-Angle Cameras array necessitates the integration of more than 100 hardware devices,100 servers,and 2500 software modules that must be synchronized within a 3-second imaging cycle.However,the complexity of real-time,high-concurrency processing of large datasets has historically resulted in substantial failure rates,with an observation efficiency estimated at less than 50%in 2023.To mitigate these challenges,we developed a monitoring system designed to improve fault diagnosis efficiency.It includes two innovative monitoring views for“state evolution”and“transient lifecycle”.Combining these with“instantaneous state”and“key parameter”monitoring views,the system represents a comprehensive monitoring strategy.Here we detail the system architecture,data collection methods,and design philosophy of the monitoring views.During one year of fault diagnosis experimental practice,the proposed system demonstrated its ability to identify and localize faults within minutes,achieving fault localization nearly ten times faster than traditional methods.Additionally,the system design exhibited high generalizability,with possible applicability to other telescope array systems.
基金supported by the National Natural Science Foundation of China(12064027,12464010)2022 Jiangxi Province High-level and Highskilled Leading Talent Training Project Selected(No.63)+1 种基金Jiujiang"Xuncheng Talents"(No.JJXC2023032)Jiujiang Natural Science Foundation Project(Key Technologies Research on Autonomous Cruise Solar-Powered UAV-2025-1).
文摘This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.
基金supported by the Open Fund of Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003)the Natural Science Foundation of Anhui Province(No.228085ME142)Comprehensive Research Facility for Fusion Technology(No.20180000527301001228)。
文摘This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.
文摘As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.
基金the National Research and InnovationAgency of Indonesia (BRIN) under research grant Rumah Program Kebencanaan 2022-2025support from the Earth Observatory Singapore (EOS)supported by the Ministry of Higher Education, Science,and Technology, and Institut Teknologi Bandung through the Indonesian Collaborative Research Program.
文摘The Lembang Fault is a major geological feature in West Java that borders the northern edge of Bandung(one of Indonesia’s largest cities).It lies just south of the active Tangkuban Perahu Volcano,exhibiting clear geomorphic signs of recent activity,and has been scientifically confirmed as active through geological and geophysical studies.In this work,we describe an Integrated along the Lembang Fault,which can be used for geodynamic research in Indonesia.We discuss the design of a seismic and Global Navigation Satellite System(GNSS)array sensor network for continuous monitoring,and report the status of monitoring stations that periodically collect highly accurate,continuous seismographic and GNSS readings,transmitting these data to a central server in Bandung for post-processing.Solutions from the array data are used to provide precise measurements of the deformation of the Earth’s surface over large distances,allowing for spatio-temporal tracking of tectonic movement,and resulting in a better understanding of seismic events in the region.In this study,our investigation revealed a significant compression rate of an estimated 13 microstrain/yr along the Lembang Fault,whereas the strain rate is much smaller farther south of the fault.This study presents the design of a seismo-geodetic observatory network that can be implemented in earthquake-prone regions for mitigation purposes,with particular utility for studying other active faults that also traverse populated areas in Indonesia.
基金funded by the National Natural Science Foundation of China(Grant No.51574225)Shandong Energy Group(Grant No.SNKJ2022BJ03-R28)for Caiping Lu+1 种基金the Research Team on MonitoringActivity Mechanisms of Unnatural Earthquakes of Shandong Earthquake Agency(Grant No.TD202301)for Chengyu Liu.
文摘Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study employed a multi-local seismic monitoring network,integrating both in-mine and local instruments at overlapping length scales.We specifically focused on a damaging local magnitude(ML)2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province,China.Moment tensor(MT)inversion revealed a complex cascading rupture mechanism:an initial moment magnitude(M_(w))2.2 normal fault slip along the DF60 fault in an ESEeWNW direction,transitioning to a M_(w)3.0 event as the FD24 and DF60 faults unclamped.The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations,providing valuable insights into the driving mechanism of these seismic sequences.The in-mine network,constrained by its low dynamic changes,captured only the nucleation phase of the DF60 fault.Furthermore,standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture.To enhance safety and risk management in mining environments,we examined the implications of source reactivation within the cluster area post-stress-adjustment.This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity.The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.
基金supported by the Program of National Natural Science Foundation of China(U23A20329,62163036)Youth Academic and Technical Leaders Reserve Talent Training project(202105AC160094)Industrial Innovation Talent Special Project of Xingdian Talent Support Program(XDYC-CYCX-2022-0010).
文摘Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.
基金The National Natural Science Foundation of China(No60504033)
文摘A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.
文摘The unique of using industrial LAN based on field bus to construct the system of vibration monitoring and fault diagnosis is introduced. The LAN topology, client/server architecture, database and designing of application software for vibration monitoring and fault diagnosis are involved. How to apply industrial LAN to the vibration monitoring and fault diagnosis of turbo generator is discussed, and a scheme of how to construct the industrial LAN for vibration monitoring and fault diagnosis of turbo generator is presented.
基金Supported by National Natural Science Foundation of China(Grant No.51675098)
文摘Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.
文摘The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vibration value of main sea water pump in the horizontal direction is abnormally high and malfunctions usually happen. Therefore, it is essential to make fault diagnosis of main sea water pump, By conventional off-line monitoring and vibration amplitude spectrum analysis, the fault cycle is found and the alarm value and stop value of equipment are set, which is helpful to equipment maintenance and accident prevention.
基金supported by the National Basic Research Program of China (No.2011CB707004)the National Natural Science Foundation of China (No.61179054)
文摘Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation to continue in the presence of fault measurement.Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly.However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage.In this paper, the impact of leverage on the fault identification is analyzed.The leveraged RAIM fault identification（L-RAIM FI） method is proposed with consideration of the difference in leverage for each satellite in view.Furthermore,the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method.The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.
基金supported by China Natural Scientific and Technological Support Projects(Wenchuan Fault Scientific Drilling)National Natural Scientific Foundation of China(Grant No.41204047)
文摘With the improvement of seismic observation system, more and more observations indicate that earthquakes may cause seismic velocity change. However, the amplitude and spatial distribution of the velocity variation remains a controversial issue. Recent active source monitoring carried out adjacent to Wenchuan Fault Scientific Drilling （WFSD） revealed unambiguous coseismic velocity change associated with a local M8 5.5 earthquake. Here, we carry out forward modeling using two-dimensional spectral element method to further investigate the amplitude and spatial distribution of observed velocity change. The model is well constrained by results from seismic reflection and WFSD coring. Our model strongly suggests that the observed coseismic velocity change is localized within the fault zone with width of ~ 120 m rather than dynamic strong ground shaking. And a velocity decrease of -2.0 % within the fault zone is required to fit the observed travel time delay distribution, which coincides with rock mechanical experiment and theoretical modeling.
基金Supported by National Natural Science Fund Project(51275052)Key project supported by Beijing Municipal Natural Science Foundation(3131002)Open topic of Key Laboratory of Key Laboratory of Modern Measurement & Control Technology,Ministry of Education(KF20141123202,KF20111123201)
文摘Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of modern, high-end and key electromechanical equipment, this paper will describe the early faults prediction method for multi-type electromechanical systems, which is favorable for predicting early faults of complex electromechanical systems in non-stationary, nonlinear, variable working conditions and long-time running state; the paper shall introduce the reconfigurable integration technology of series safety monitoring systems based on which the integrated development platform of series safety monitoring systems is built. This platform can adapt to integrated R&D of series safety monitoring systems characterized by high technology, multiple species and low volume. With the help of this platform, series safety monitoring systems were developed, and the Remote Network Security Monitoring Center for Facility Groups was built. Experimental research and engineering applications show that: this new fault prediction method has realized the development trend features extraction of typical electromechanical systems, multi-information fusion, intelligent information decision-making and so on, improving the processing accuracy, relevance and applicability of information; new reconfigurable integration technologies have improved the integration level and R&D efficiency of series safety monitoring systems as well as expanded the scope of application; the series safety monitoring systems developed based on reconfigurable integration platform has already played an important role in many aspects including ensuring safety operation of equipment, stabilizing product quality, optimizing running state, saving energy consumption, reducing environmental pollution, improving working conditions, carrying out scientific maintenance, advancing equipment utilization, saving maintenance charge and enhancing the level of information management.
基金supported by the National Natural Science Foundation of China (6117419791016019)+1 种基金the Nanjing University of Aeronautics and Astronautics Research Foundation (NP2011049NZ2012003)
文摘Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system （RINS） in airborne navigation systems. An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertial navigation system （INS）. On this basis, the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS. The simulation is carried out based on the above algorithms, and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering. Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.
