Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of ble...Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.展开更多
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
Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on parti...Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.展开更多
The wMPS is a laser-based measurement system used for large scale metrology.However,it is susceptible to external factors such as vibrations,which can lead to unreliable measurements.This paper presents a fault diagno...The wMPS is a laser-based measurement system used for large scale metrology.However,it is susceptible to external factors such as vibrations,which can lead to unreliable measurements.This paper presents a fault diagnosis and separation method which can counter this problem.To begin with,the paper uses simple models to explain the fault diagnosis and separation methods.These methods are then mathematically derived using statistical analysis and the principles of the wMPS.A comprehensive solution for fault diagnosis and separation is proposed,considering the characteristics of the wMPS.The effectiveness of this solution is verified through experimental observations.It can be concluded that this approach can detect and separate false observations,thereby enhancing the reliability of the wMPS.展开更多
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th...In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.展开更多
With the application of Distributed Acoustic Sensors(DAS)across various infrastructures,it will play a pivotal role in shaping smart cities in the future.However,the current single-source detection and identification ...With the application of Distributed Acoustic Sensors(DAS)across various infrastructures,it will play a pivotal role in shaping smart cities in the future.However,the current single-source detection and identification technology might struggle to meet the high precision needs in the intricate environmental conditions of mixed multi-source interference.We propose a new deep neural network-based multi-source signal separation method for DAS and accomplish the separation performance of this method under practical applications.In addition,a new evaluation metric for the separation method is proposed in conjunction with the separation and identification of DAS mixed signals.For mixed signals with different source numbers,the recognizable rate of separated signals can reach 98.33%on average.This study provides a promising solution to the multi-source mixed interference problem faced by DAS in complex environments.展开更多
In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic ano...In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic anomaly qualitatively, integrating geological structure features in the area. In particular, we studied the spatial distributions of the two main faults called Shunyi-Liangxiang fault and Banqiao-Babaoshan-Tongxian fault, which have cut and gone through the central Beijing area striking in NE and EW directions, respectively. The influences of these two faults on the earthquakes have also been discussed briefly.展开更多
Blind source separation (BSS) technology is very useful in many fields, such as communication, radar and so on. Because of the advantage of BSS that it can separate multi-sources even not knowing the mix-coefficient a...Blind source separation (BSS) technology is very useful in many fields, such as communication, radar and so on. Because of the advantage of BSS that it can separate multi-sources even not knowing the mix-coefficient and the probability distribution, it can also be used in fault diagnosis. In this paper, we first use the BSS to deal with the sound from the machinery in fault diagnosis. We make a simulation of two sound sources and four sensors to test the result. Each source is a narrow-band source, which is composed of several sine waves. The result shows that the two sources can be well separated from the mixed signals. So we can draw a conclusion that BSS can improve the technology of sound fault diagnosis, especially in rotating machinery.展开更多
Dependable computer based systems employing fault tolerance and robust software development techniques demand additional error detection and recovery related tasks. This results in tangling of core functionality with ...Dependable computer based systems employing fault tolerance and robust software development techniques demand additional error detection and recovery related tasks. This results in tangling of core functionality with these cross cutting non-functional concerns. In this regard current work identifies these dependability related non-functional and cross-cutting concerns and proposes design and implementation solutions in an aspect oriented framework that modularizes and separates them from core functionality. The degree of separation has been quantified using software metrics. A Lego NXT Robot based case study has been completed to evaluate the proposed design framework.展开更多
This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity an...This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity and reducing the randomicity of cyclostationary signal and it is particularly applicable to the separation of low order cyclostationary signals.The method also demonstrates the importance of extraction of cyclostationary signals from low order to high order in turn.The effectiveness of the proposed method is finally demonstrated by computer simulation and experiment.展开更多
The main faults of dish centrifugal separator's helical gear are described inthis paper. In order to diagnose the DRJ-460 dish centrifugal separator correctly, the vibration istested with a helical gear under both...The main faults of dish centrifugal separator's helical gear are described inthis paper. In order to diagnose the DRJ-460 dish centrifugal separator correctly, the vibration istested with a helical gear under both normal and abnormal conditions. After comparing severalgeneral methods of the gear's fault feature extraction, a new convenient and effective method ispresented on the basis of analyzing the vibration spectrum under different rotary velocities.展开更多
Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable opera...Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.展开更多
As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-en...As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-enhanced dual-stream network (MemFuse-DSN). The method reconstructs the feature space by selecting and enhancing multi-source domain samples based on similarity metrics. An adaptive weighted dual-stream architecture is designed, integrating gradient reversal and orthogonality constraints to achieve efficient feature alignment. In addition, a novel dual dynamic memory module is introduced: the task memory bank is used to store high-confidence class prototype information, and adopts an exponential moving average (EMA) strategy to ensure the smooth evolution of prototypes over time;the domain memory bank is periodically updated and clusters potential noisy features, dynamically tracking domain shift trends, thereby optimizing the decoupled feature learning process. Experimental validation was conducted on a ±110 kV transformer vibration testing platform using typical fault types including winding looseness, core looseness, and compound faults. The results show that the proposed method achieves a fault diagnosis accuracy of 99.2%, providing a highly generalizable solution for the intelligent operation and maintenance of power equipment.展开更多
Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy waste.Feature selection(FS)is a critical prerequisite for effective FD.However,current FS methods have two major ga...Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy waste.Feature selection(FS)is a critical prerequisite for effective FD.However,current FS methods have two major gaps.First,most approaches rely on single-source ranking information(SSRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI.Second,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic performance.To address these issues,a robust ensemble FS method based on multi-source ranking information(MSRI)is proposed.By employing an efficient strategy based on maximizing relevance while proper redundancy,the MSRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative perspectives.Additionally,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library.From a methodological standpoint,a general framework for constructing the MSRI-based FS method is provided.The proposed method is applied to chiller FD and tested across ten widely-used machine learning models.Thirteen optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MSRI method.Compared to the SSRI approach,the MSRI method shows superior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MSRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.展开更多
Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of col...Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.展开更多
This paper proposes an adaptive sliding mode observer(ASMO)-based approach for wind turbines subject to simultaneous faults in sensors and actuators.The proposed approach enables the simultaneous detection of actuator...This paper proposes an adaptive sliding mode observer(ASMO)-based approach for wind turbines subject to simultaneous faults in sensors and actuators.The proposed approach enables the simultaneous detection of actuator and sensor faults without the need for any redundant hardware components.Additionally,wind speed variations are considered as unknown disturbances,thus eliminating the need for accurate measurement or estimation.The proposed ASMO enables the accurate estimation and reconstruction of the descriptor states and disturbances.The proposed design implements the principle of separation to enable the use of the nominal controller during faulty conditions.Fault tolerance is achieved by implementing a signal correction scheme to recover the nominal behavior.The performance of the proposed approach is validated using a 4.8 MW wind turbine benchmark model subject to various faults.Monte-Carlo analysis is also carried out to further evaluate the reliability and robustness of the proposed approach in the presence of measurement errors.Simplicity,ease of implementation and the decoupling property are among the positive features of the proposed approach.展开更多
High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faul...High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.展开更多
This paper investigates the problem of two-stage extended Kalman filter （TSEKF）-based fault estimation for reaction flywheels in satellite attitude control systems （ACSs）. Firstly, based on the separate-bias princ...This paper investigates the problem of two-stage extended Kalman filter （TSEKF）-based fault estimation for reaction flywheels in satellite attitude control systems （ACSs）. Firstly, based on the separate-bias principle, a satellite ACSs with actuator fault is transformed into an augmented nonlinear discrete stochastic model; then, a novel TSEKF is suggested such that it can simultane- ously estimate satellite attitude information and actuator faults no matter they are additive or mul- tiplicative; finally, the proposed approach is respectively applied to estimating bias faults and loss of effectiveness for reaction flywheels in satellite ACSs, and simulation results demonstrate the effec- tiveness of the proposed fault estimation approach.展开更多
Fault-related parameters are critical for studying tectonic evolution, deformation character- istics, active tectonism, and seismic hazards. A new method of calculating reverse-fault- related parameters has been devel...Fault-related parameters are critical for studying tectonic evolution, deformation character- istics, active tectonism, and seismic hazards. A new method of calculating reverse-fault- related parameters has been developed, which uses systematic analysis of the geometrical characteristics of normal and reverse scarps of reverse faults together with measurements of topographic profiles and fault bedding. The results show that the most suitable method of calculating fault parameters heavily relies on the specific type of fault scarp. For a reverse scarp, the size of the vertical displacement （VD） of the fault, the vertical separation （VS） of the hanging wall and the footwall, and the fault scarp height （SH）how the relationship VD ≥VS ≥ SH; conversely, for normal scarps, VD ≤ VS ≤ SH. The theoretical equations were used to study fault deformation in the Southwest Tianshan Mountain foreland basin. The results showed that, for every fault, VD ≥ VS ≥SH, which is consistent with our predicted relationship. This finding demonstrates that this method is suitable to explore structural information of reverse faults. In the study area, the vertical displacement is 1.4 times the horizontal displacement, suggesting that fiexural-slip faults may play an important role in transferring local deformation from horizontal shortening to vertical uplift. Therefore, one of the most important steps in correct calculation of reverse-fault-related parameters is selection of the proper equations by identifying the specific type of fault scarp and the corresponding calculation method.展开更多
The rate of vertical differential movement of the great Weihe fault (west segment) in different periods is analyzed by using the data of the historic evolution of the Zhouyuan plateau surface. Results show that the ra...The rate of vertical differential movement of the great Weihe fault (west segment) in different periods is analyzed by using the data of the historic evolution of the Zhouyuan plateau surface. Results show that the rate reached a maximum in the Ming Dynasty, about 6. 4 mm/a, which corresponded well to the period of strongearthquake on the Wei River fault in the 15-16th centuries. Based on such a correspondence, the time separation between active periods of Ms=8. 0 strong earthquakes in the Wei River fault depression is investigated.展开更多
文摘Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.
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Project(50875247) supported by the National Natural Science Foundation of ChinaProject(2007011070) supported by the Natural Science Foundation of Shanxi Province, China
文摘Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.
文摘The wMPS is a laser-based measurement system used for large scale metrology.However,it is susceptible to external factors such as vibrations,which can lead to unreliable measurements.This paper presents a fault diagnosis and separation method which can counter this problem.To begin with,the paper uses simple models to explain the fault diagnosis and separation methods.These methods are then mathematically derived using statistical analysis and the principles of the wMPS.A comprehensive solution for fault diagnosis and separation is proposed,considering the characteristics of the wMPS.The effectiveness of this solution is verified through experimental observations.It can be concluded that this approach can detect and separate false observations,thereby enhancing the reliability of the wMPS.
基金supported by the Science and Technology Project of State Grid Shandong Electric Power Company?“Research on the Data-Driven Method for Energy Internet”?(Project No.2018A-100)。
文摘In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.
文摘With the application of Distributed Acoustic Sensors(DAS)across various infrastructures,it will play a pivotal role in shaping smart cities in the future.However,the current single-source detection and identification technology might struggle to meet the high precision needs in the intricate environmental conditions of mixed multi-source interference.We propose a new deep neural network-based multi-source signal separation method for DAS and accomplish the separation performance of this method under practical applications.In addition,a new evaluation metric for the separation method is proposed in conjunction with the separation and identification of DAS mixed signals.For mixed signals with different source numbers,the recognizable rate of separated signals can reach 98.33%on average.This study provides a promising solution to the multi-source mixed interference problem faced by DAS in complex environments.
基金National Development and Reform Commission Project ″Experimental Detection of Urban Active Faults″ (2004-1138).
文摘In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic anomaly qualitatively, integrating geological structure features in the area. In particular, we studied the spatial distributions of the two main faults called Shunyi-Liangxiang fault and Banqiao-Babaoshan-Tongxian fault, which have cut and gone through the central Beijing area striking in NE and EW directions, respectively. The influences of these two faults on the earthquakes have also been discussed briefly.
文摘Blind source separation (BSS) technology is very useful in many fields, such as communication, radar and so on. Because of the advantage of BSS that it can separate multi-sources even not knowing the mix-coefficient and the probability distribution, it can also be used in fault diagnosis. In this paper, we first use the BSS to deal with the sound from the machinery in fault diagnosis. We make a simulation of two sound sources and four sensors to test the result. Each source is a narrow-band source, which is composed of several sine waves. The result shows that the two sources can be well separated from the mixed signals. So we can draw a conclusion that BSS can improve the technology of sound fault diagnosis, especially in rotating machinery.
文摘Dependable computer based systems employing fault tolerance and robust software development techniques demand additional error detection and recovery related tasks. This results in tangling of core functionality with these cross cutting non-functional concerns. In this regard current work identifies these dependability related non-functional and cross-cutting concerns and proposes design and implementation solutions in an aspect oriented framework that modularizes and separates them from core functionality. The degree of separation has been quantified using software metrics. A Lego NXT Robot based case study has been completed to evaluate the proposed design framework.
基金Doctor Foundations of Henan polytechnic university(648391)NSFC(U1304523,51205371)
文摘This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity and reducing the randomicity of cyclostationary signal and it is particularly applicable to the separation of low order cyclostationary signals.The method also demonstrates the importance of extraction of cyclostationary signals from low order to high order in turn.The effectiveness of the proposed method is finally demonstrated by computer simulation and experiment.
基金paper is sponsored by the Foundation of Donghua University
文摘The main faults of dish centrifugal separator's helical gear are described inthis paper. In order to diagnose the DRJ-460 dish centrifugal separator correctly, the vibration istested with a helical gear under both normal and abnormal conditions. After comparing severalgeneral methods of the gear's fault feature extraction, a new convenient and effective method ispresented on the basis of analyzing the vibration spectrum under different rotary velocities.
文摘Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.
基金supported by the State Grid Shandong Electric Power Company Project(Grant Number SGSDJX00BDJS2400388).
文摘As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-enhanced dual-stream network (MemFuse-DSN). The method reconstructs the feature space by selecting and enhancing multi-source domain samples based on similarity metrics. An adaptive weighted dual-stream architecture is designed, integrating gradient reversal and orthogonality constraints to achieve efficient feature alignment. In addition, a novel dual dynamic memory module is introduced: the task memory bank is used to store high-confidence class prototype information, and adopts an exponential moving average (EMA) strategy to ensure the smooth evolution of prototypes over time;the domain memory bank is periodically updated and clusters potential noisy features, dynamically tracking domain shift trends, thereby optimizing the decoupled feature learning process. Experimental validation was conducted on a ±110 kV transformer vibration testing platform using typical fault types including winding looseness, core looseness, and compound faults. The results show that the proposed method achieves a fault diagnosis accuracy of 99.2%, providing a highly generalizable solution for the intelligent operation and maintenance of power equipment.
基金the National Natural Science Foundation of China(No.52478087)China Postdoctoral Science Foundation(No.2024M750799,No.2024T170238)+4 种基金China Scholarship Council(No.202308410494)Zhongyuan Outstanding Youth Talent Program(No.2022 Year)Youth Scientist Project in Henan Province(No.225200810087)the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.22HASTIT025)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(No.22IRTSTHN006).
文摘Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy waste.Feature selection(FS)is a critical prerequisite for effective FD.However,current FS methods have two major gaps.First,most approaches rely on single-source ranking information(SSRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI.Second,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic performance.To address these issues,a robust ensemble FS method based on multi-source ranking information(MSRI)is proposed.By employing an efficient strategy based on maximizing relevance while proper redundancy,the MSRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative perspectives.Additionally,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library.From a methodological standpoint,a general framework for constructing the MSRI-based FS method is provided.The proposed method is applied to chiller FD and tested across ten widely-used machine learning models.Thirteen optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MSRI method.Compared to the SSRI approach,the MSRI method shows superior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MSRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.
基金supported by the Major National Science and Technology Projects(No.2017-IV-0008-0045)the National Natural Science Foundation of China(Nos.51675262 and 51975276)+1 种基金the Advance Research Field Fund Project of China(No.61400040304)the National Key Research and Development Program of China(No.2018YFB2003300)。
文摘Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.
文摘This paper proposes an adaptive sliding mode observer(ASMO)-based approach for wind turbines subject to simultaneous faults in sensors and actuators.The proposed approach enables the simultaneous detection of actuator and sensor faults without the need for any redundant hardware components.Additionally,wind speed variations are considered as unknown disturbances,thus eliminating the need for accurate measurement or estimation.The proposed ASMO enables the accurate estimation and reconstruction of the descriptor states and disturbances.The proposed design implements the principle of separation to enable the use of the nominal controller during faulty conditions.Fault tolerance is achieved by implementing a signal correction scheme to recover the nominal behavior.The performance of the proposed approach is validated using a 4.8 MW wind turbine benchmark model subject to various faults.Monte-Carlo analysis is also carried out to further evaluate the reliability and robustness of the proposed approach in the presence of measurement errors.Simplicity,ease of implementation and the decoupling property are among the positive features of the proposed approach.
基金supported by the State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks(No.SGNR0000KJJS2302137)the National Natural Science Foundation of China(Grant No.62203248)the Natural Science Foundation of Shandong Province(Grant No.ZR2020ME194).
文摘High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.
文摘This paper investigates the problem of two-stage extended Kalman filter （TSEKF）-based fault estimation for reaction flywheels in satellite attitude control systems （ACSs）. Firstly, based on the separate-bias principle, a satellite ACSs with actuator fault is transformed into an augmented nonlinear discrete stochastic model; then, a novel TSEKF is suggested such that it can simultane- ously estimate satellite attitude information and actuator faults no matter they are additive or mul- tiplicative; finally, the proposed approach is respectively applied to estimating bias faults and loss of effectiveness for reaction flywheels in satellite ACSs, and simulation results demonstrate the effec- tiveness of the proposed fault estimation approach.
基金supported by the Science and Technology Program of Shanxi Province(2014KJXX-18)the Spark Programs of Earthquake Sciences(XH14069)
文摘Fault-related parameters are critical for studying tectonic evolution, deformation character- istics, active tectonism, and seismic hazards. A new method of calculating reverse-fault- related parameters has been developed, which uses systematic analysis of the geometrical characteristics of normal and reverse scarps of reverse faults together with measurements of topographic profiles and fault bedding. The results show that the most suitable method of calculating fault parameters heavily relies on the specific type of fault scarp. For a reverse scarp, the size of the vertical displacement （VD） of the fault, the vertical separation （VS） of the hanging wall and the footwall, and the fault scarp height （SH）how the relationship VD ≥VS ≥ SH; conversely, for normal scarps, VD ≤ VS ≤ SH. The theoretical equations were used to study fault deformation in the Southwest Tianshan Mountain foreland basin. The results showed that, for every fault, VD ≥ VS ≥SH, which is consistent with our predicted relationship. This finding demonstrates that this method is suitable to explore structural information of reverse faults. In the study area, the vertical displacement is 1.4 times the horizontal displacement, suggesting that fiexural-slip faults may play an important role in transferring local deformation from horizontal shortening to vertical uplift. Therefore, one of the most important steps in correct calculation of reverse-fault-related parameters is selection of the proper equations by identifying the specific type of fault scarp and the corresponding calculation method.
文摘The rate of vertical differential movement of the great Weihe fault (west segment) in different periods is analyzed by using the data of the historic evolution of the Zhouyuan plateau surface. Results show that the rate reached a maximum in the Ming Dynasty, about 6. 4 mm/a, which corresponded well to the period of strongearthquake on the Wei River fault in the 15-16th centuries. Based on such a correspondence, the time separation between active periods of Ms=8. 0 strong earthquakes in the Wei River fault depression is investigated.
