Fractal dimensions of a terrain quantitatively describe the self-organizedstructure of the terrain geometry. However, the local topographic variation cannot be illustrated bythe conventional box-counting method. This ...Fractal dimensions of a terrain quantitatively describe the self-organizedstructure of the terrain geometry. However, the local topographic variation cannot be illustrated bythe conventional box-counting method. This paper proposes a successive shift box-counting method,in which the studied object is divided into small sub-objects that are composed of a series of gridsaccording to its characteristic scaling. The terrain fractal dimensions in the grids are calculatedwith the successive shift box-counting method and the scattered points with values of fractaldimensions are obtained. The present research shows that the planar variation of fractal dimensionsis well consistent with fault traces and geological boundaries.展开更多
This study combined fault identification with a deep learning algorithm and applied a convolutional neural network(CNN)design based on an improved balanced crossentropy(BCE)loss function to address the low accuracy in...This study combined fault identification with a deep learning algorithm and applied a convolutional neural network(CNN)design based on an improved balanced crossentropy(BCE)loss function to address the low accuracy in the intelligent identification of seismic faults and the slow training speed of convolutional neural networks caused by unbalanced training sample sets.The network structure and optimal hyperparameters were determined by extracting feature maps layer by layer and by analyzing the results of seismic feature extraction.The BCE loss function was used to add the parameter which is the ratio of nonfaults to the total sample sets,thereby changing the loss function to find the reference of the minimum weight parameter and adjusting the ratio of fault to nonfault data.The method overcame the unbalanced number of sample sets and improved the iteration speed.After a brief training,the accuracy could reach more than 95%,and gradient descent was evident.The proposed method was applied to fault identification in an oilfield area.The trained model can predict faults clearly,and the prediction results are basically consistent with an actual case,verifying the effectiveness and adaptability of the method.展开更多
Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification ...Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.展开更多
In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the ...In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the presence of possible nonidentical and unknown direction faults(NUDFs) in the yaw channel.The control strategy begins with the design of a pre-specified performance event-triggered observer for each individual UAV.These observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer output.It is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.展开更多
According to the background of the rub impact faults of aerial engines and industrial turbines, two kinds of test rigs, on the base of the dynamics model, are established to study the rub impact faults between rotor...According to the background of the rub impact faults of aerial engines and industrial turbines, two kinds of test rigs, on the base of the dynamics model, are established to study the rub impact faults between rotor and stator with free supports. The orbit of the vibration of rotor displacement is respectively examined on the four impact conditions, which are the normal state with no impact, the early sharp impact statement, the semi sharp impact statement and the terminal blunt impact statement. The route to chaos, appearing with the early sharp impact, is observed for the first time. By analyzing the frequency domain characteristics of the experimental data on four impact conditions, it is testified that the appearance of the sub harmonic vibrations of the order 1/3 and 1/4 is the effective evidence to judge whether or not the blade has initial light rub impact. When there are only the harmonic vibrations of the order of 1/1 and 1/2, the blade stator rub impact faults have become very serious.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
Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identific...Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.展开更多
The intermittent connection（IC）of the field-bus in networked manufacturing systems is a common but hard troubleshooting network problem,which may result in system level failures or safety issues.However,there is no ...The intermittent connection（IC）of the field-bus in networked manufacturing systems is a common but hard troubleshooting network problem,which may result in system level failures or safety issues.However,there is no online IC location identification method available to detect and locate the position of the problem.To tackle this problem,a novel model based online fault location identification method for localized IC problem is proposed.First,the error event patterns are identified and classified according to different node sources in each error frame.Then generalized zero inflated Poisson process（GZIP）model for each node is established by using time stamped error event sequence.Finally,the location of the IC fault is determined by testing whether the parameters of the fitted stochastic model is statistically significant or not using the confident intervals of the estimated parameters.To illustrate the proposed method,case studies are conducted on a 3-node controller area network（CAN）test-bed,in which IC induced faults are imposed on a network drop cable using computer controlled on-off switches.The experimental results show the parameters of the GZIP model for the problematic node are statistically significant（larger than 0）,and the patterns of the confident intervals of the estimated parameters are directly linked to the problematic node,which agrees with the experimental setup.The proposed online IC location identification method can successfully identify the location of the drop cable on which IC faults occurs on the CAN network.展开更多
Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme le...Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme learning machine(ELM)is proposed.Firstly,the original signal is decomposed by wavelet packet,and correlation coefficients between the reconstructed signal of each sub-band and the original signal as well as the energy entropy of each sample are obtained.Then,the eigenvectors established by the correlation coefficients method and the energy entropy method fused with kurtosis are inputted to the four kinds of classifiers including BP neural network,KNN classifier,support vector machine and ELM respectively for training and testing.Experimental results show that the method proposed in this paper can effectively reflect the differences that the fault produces and identify the single-cylinder misfire accurately,which has the advantages of higher accuracy and shorter training time.展开更多
Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-ti...Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic(PV)inverters.Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere.Power converters represent the main parts for the grid integration of PV systems.However,PV power converters contain several power switches that construct their circuits.The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions.Moreover,the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously.Consequently,the grid-tied PV systems have a nonlinear factor and the fault detection and identification(FDI)methods based on using mathematical models become more complex.The proposed fuzzy logic-based FDI(FL-FDI)method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults.The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes.Therefore,the proposed FL-FDI method does not require additional components or measurement circuits.Additionally,the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes.The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models.The proposed FL-FDI method is tested on the widely used T-type PV inverter system,wherein there are twelve different switches and the FDI process represents a challenging task.The results shows the superior and accurate performance of the proposed FL-FDI method.展开更多
Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to compon...Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.展开更多
Fault management is crucial to pro vi de quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, wh...Fault management is crucial to pro vi de quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in mana gement information base, the algorithm is compatible with the existing simple ne twork management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identif iers are acquired from the network simulation. A k-nearest neighbor classif ier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, which prove the accuracy of the algo rithm. The identification errors are discussed to illustrate that the novel faul t identification algorithm is adaptive in the fault scenarios with network traff ic change.展开更多
Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identi...Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.展开更多
A diagnostic signal current trace detecting based single phase-to-ground fault line identifica- tion and section location method for non-effectively grounded distribution systems is presented in this paper.A special d...A diagnostic signal current trace detecting based single phase-to-ground fault line identifica- tion and section location method for non-effectively grounded distribution systems is presented in this paper.A special diagnostic signal current is injected into the fault distribution system,and then it is detected at the outlet terminals to identify the fault line and at the sectionalizing or branching point along the fault line to locate the fault section.The method has been put into application in actual distribution network and field experience shows that it can identify the fault line and locate the fault section correctly and effectively.展开更多
A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stab...A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.展开更多
In this paper, we suggest a novel parsimonious neurofuzzy model realized by RBFNs for railway carriage system identification and fault diagnosis. To overcome the curse of dimensionality resulting from high dimensional...In this paper, we suggest a novel parsimonious neurofuzzy model realized by RBFNs for railway carriage system identification and fault diagnosis. To overcome the curse of dimensionality resulting from high dimensional input variables, in our developed model the features extracted from the available observations are regarded as the input variables by adopting the higher-order statistics(HOS) technique. Such a constructed model is also applied to a practical railway carriage system, simulation results indicate that the developed neurofuzzy model possesses strong identification and fault diagnosis ability.展开更多
The Sichuan Basin has abundant oil and gas resources and it has been generally acknowledged as a super petroliferous basin.In recent years,immense exploration and development potential of deep natural gas emerges in t...The Sichuan Basin has abundant oil and gas resources and it has been generally acknowledged as a super petroliferous basin.In recent years,immense exploration and development potential of deep natural gas emerges in the Sichuan Basin.The identification of strikeeslip faults and their reservoir controlling effects are of great significance to develop a new exploration domain of fault-controlled gas reservoirs.In order to clarify the distribution of strikeeslip faults in the Sichuan Basin and their relationships with natural gas exploration,in view that the strikeeslip faults in this basin are small and complex with poor seismic data and influenced more by the fold deformation of Triassic gypsum salt beds,this paper identifies strikeeslip faults and analyzes their characteristics,distribution and genesis based on the interpretation of continuous 3D seismic data of the central Sichuan Basin.Then,the fault-controlled gas reservoir model of strike-slip fault is established by reviewing old wells and evaluating geological conditions of hydrocarbon accumulation.Finally,the exploration potential and exploration direction of fault-controlled gas reservoirs are discussed.And the following research results are obtained.First,intraplate weak strikeeslip faults can be identified by combining the seismic profile marker of strikeeslip fault with the plane marker.Second,eight large strikeeslip fault zones with a total length of 1280 km are identified and confirmed in the central Sichuan Basin.Third,strikeeslip faults are mainly distributed in the SinianePermian System,mainly composed of small-displacement oblique faults and echelon faults,and different in stratification,grading,classification and segmentation.Fourth,under the control of the oblique subduction of the Proto-Tethys Ocean and the different structures of the basement,the strikeeslip faults in the central Sichuan Basin were formed in the Sinian and there are multiple stages of inherited CaledonianeHercynian activities.Fifth,the strikeeslip faults in the central Sichuan Basin play a role in sourceereservoir connection,reservoir stimulation,and gas accumulation and high yield,and constitute the fault-controlled natural gas accumulation system of“three-element controlling reservoir and composite hydrocarbon accumulation”based on source rock,fault and reservoir.In conclusion,a large intraplate strikeeslip fault system is developed in the Sichuan Basin,which has the geological conditions of hydrocarbon accumulation to form strikeeslip fault-controlled large gas field,and the large source connecting strike-slip fault zones are the main breakthrough directions of natural gas exploration in fault-controlled carbonate rocks.展开更多
Nowadays, the DC distribution system has been suggested, as a replacement for the AC power distribution system with electric propulsion. This idea signifies a fresh approach of issuing energy for low-voltage installat...Nowadays, the DC distribution system has been suggested, as a replacement for the AC power distribution system with electric propulsion. This idea signifies a fresh approach of issuing energy for low-voltage installations. It can be used for any electrical application up to 20 MW and works at a nominal voltage of 1000 V DC. The DC distribution system is just an extension of the multiple DC links that previously available in all propulsion and thruster drives, which typically comprise more than 80% of the electrical power consumption on electric propulsion vessels. A fault detection and islanding scheme for DC grid connected PV system is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of this paper are to classify and detect the fault in the PV system as well as DC grid and to isolate the faulted section so that the system keeps operating without disabling the entire system. The results show the measured values of power at PV panel and DC grid side under different fault condition, which indicates the type of fault that occurs in the system.展开更多
Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency,reduce maintenance costs,extend their lifespan,and enhance reliability in the wind energy sector.This is particular...Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency,reduce maintenance costs,extend their lifespan,and enhance reliability in the wind energy sector.This is particularly necessary in offshore wind,currently one of the most critical assets for achieving sustainable energy generation goals,due to the harsh marine environment and the difficulty of maintenance tasks.To address this problem,this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines,using normalized and linearized operational data.The proposed framework transforms heterogeneous wind speed and power measurements into a unified scale,enabling the development of a new wind power index(WPi)that quantifies deviations from expected performance.Additionally,spatial and temporal coherence analyses of turbines within a wind farm ensure the validity of these normalized measurements across different wind turbine models and operating conditions.Furthermore,a Support Vector Machine(SVM)refines the classification process,effectively distinguishing measurement errors from actual power generation failures.Validation of this strategy using real-world data from the Alpha Ventus wind farm demonstrates that the proposed approach not only improves predictive maintenance but also optimizes energy production,highlighting its potential for broad application in offshore wind installations.展开更多
文摘Fractal dimensions of a terrain quantitatively describe the self-organizedstructure of the terrain geometry. However, the local topographic variation cannot be illustrated bythe conventional box-counting method. This paper proposes a successive shift box-counting method,in which the studied object is divided into small sub-objects that are composed of a series of gridsaccording to its characteristic scaling. The terrain fractal dimensions in the grids are calculatedwith the successive shift box-counting method and the scattered points with values of fractaldimensions are obtained. The present research shows that the planar variation of fractal dimensionsis well consistent with fault traces and geological boundaries.
基金supported by the Natural Science Foundation of Shandong Province(ZR202103050722).
文摘This study combined fault identification with a deep learning algorithm and applied a convolutional neural network(CNN)design based on an improved balanced crossentropy(BCE)loss function to address the low accuracy in the intelligent identification of seismic faults and the slow training speed of convolutional neural networks caused by unbalanced training sample sets.The network structure and optimal hyperparameters were determined by extracting feature maps layer by layer and by analyzing the results of seismic feature extraction.The BCE loss function was used to add the parameter which is the ratio of nonfaults to the total sample sets,thereby changing the loss function to find the reference of the minimum weight parameter and adjusting the ratio of fault to nonfault data.The method overcame the unbalanced number of sample sets and improved the iteration speed.After a brief training,the accuracy could reach more than 95%,and gradient descent was evident.The proposed method was applied to fault identification in an oilfield area.The trained model can predict faults clearly,and the prediction results are basically consistent with an actual case,verifying the effectiveness and adaptability of the method.
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
文摘Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
基金the Industry-University Cooperation Project in Fujian Province University(No.2022H6020)。
文摘Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.
基金supported in part by the National Natural Science Foundation of China(62173028,62233015,62173024)the Guangdong Basic and Applied Basic Research Foundation(2024A1515011493)+3 种基金the Science,Technology&Innovation Project of Xiongan New Area(2023XAGG0062)Beijing Natural Science Foundation(4232060)the International Scientists Project,Beijing Natural Science Foundation(IS23065)the Brazilian Research Council(303289/2022-8)
文摘In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the presence of possible nonidentical and unknown direction faults(NUDFs) in the yaw channel.The control strategy begins with the design of a pre-specified performance event-triggered observer for each individual UAV.These observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer output.It is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.
文摘According to the background of the rub impact faults of aerial engines and industrial turbines, two kinds of test rigs, on the base of the dynamics model, are established to study the rub impact faults between rotor and stator with free supports. The orbit of the vibration of rotor displacement is respectively examined on the four impact conditions, which are the normal state with no impact, the early sharp impact statement, the semi sharp impact statement and the terminal blunt impact statement. The route to chaos, appearing with the early sharp impact, is observed for the first time. By analyzing the frequency domain characteristics of the experimental data on four impact conditions, it is testified that the appearance of the sub harmonic vibrations of the order 1/3 and 1/4 is the effective evidence to judge whether or not the blade has initial light rub impact. When there are only the harmonic vibrations of the order of 1/1 and 1/2, the blade stator rub impact faults have become very serious.
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Financial support for carrying out this work was provided by the Shandong Provincial Key Research and Development Program(2018YFJH0802)。
文摘Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.
基金Supported by National Natural Science Foundation of China(Grant No51005205)Science Fund for Creative Research Groups of Nationa Natural Science Foundation of China(Grant No.51221004)+1 种基金Nationa Basic Research Program of China(973 Program,Grant No.2013CB035405)Open Foundation of State Key Laboratory of Automotive Safety and Energy,Tsinghua University,China(Grant No.KF13011)
文摘The intermittent connection（IC）of the field-bus in networked manufacturing systems is a common but hard troubleshooting network problem,which may result in system level failures or safety issues.However,there is no online IC location identification method available to detect and locate the position of the problem.To tackle this problem,a novel model based online fault location identification method for localized IC problem is proposed.First,the error event patterns are identified and classified according to different node sources in each error frame.Then generalized zero inflated Poisson process（GZIP）model for each node is established by using time stamped error event sequence.Finally,the location of the IC fault is determined by testing whether the parameters of the fitted stochastic model is statistically significant or not using the confident intervals of the estimated parameters.To illustrate the proposed method,case studies are conducted on a 3-node controller area network（CAN）test-bed,in which IC induced faults are imposed on a network drop cable using computer controlled on-off switches.The experimental results show the parameters of the GZIP model for the problematic node are statistically significant（larger than 0）,and the patterns of the confident intervals of the estimated parameters are directly linked to the problematic node,which agrees with the experimental setup.The proposed online IC location identification method can successfully identify the location of the drop cable on which IC faults occurs on the CAN network.
基金Tianjin Regional Demonstration Project of Marine Economy Innovation and Development(No.2015120024000473)
文摘Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme learning machine(ELM)is proposed.Firstly,the original signal is decomposed by wavelet packet,and correlation coefficients between the reconstructed signal of each sub-band and the original signal as well as the energy entropy of each sample are obtained.Then,the eigenvectors established by the correlation coefficients method and the energy entropy method fused with kurtosis are inputted to the four kinds of classifiers including BP neural network,KNN classifier,support vector machine and ELM respectively for training and testing.Experimental results show that the method proposed in this paper can effectively reflect the differences that the fault produces and identify the single-cylinder misfire accurately,which has the advantages of higher accuracy and shorter training time.
基金supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project No.2020/01/11742.
文摘Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic(PV)inverters.Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere.Power converters represent the main parts for the grid integration of PV systems.However,PV power converters contain several power switches that construct their circuits.The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions.Moreover,the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously.Consequently,the grid-tied PV systems have a nonlinear factor and the fault detection and identification(FDI)methods based on using mathematical models become more complex.The proposed fuzzy logic-based FDI(FL-FDI)method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults.The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes.Therefore,the proposed FL-FDI method does not require additional components or measurement circuits.Additionally,the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes.The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models.The proposed FL-FDI method is tested on the widely used T-type PV inverter system,wherein there are twelve different switches and the FDI process represents a challenging task.The results shows the superior and accurate performance of the proposed FL-FDI method.
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)the Department of National Defence(DND)under the Discovery Grant and DND Supplemental Programs。
文摘Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.
文摘Fault management is crucial to pro vi de quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in mana gement information base, the algorithm is compatible with the existing simple ne twork management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identif iers are acquired from the network simulation. A k-nearest neighbor classif ier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, which prove the accuracy of the algo rithm. The identification errors are discussed to illustrate that the novel faul t identification algorithm is adaptive in the fault scenarios with network traff ic change.
基金This work was supported in part by the Natural Science Foundation of Henan Province,and the specific grant number is 232300420301。
文摘Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.
基金Postdoctoral Foundation of China(No.20070410755)PAN Zhencun,born in 1962,male,postdoctor researcher.
文摘A diagnostic signal current trace detecting based single phase-to-ground fault line identifica- tion and section location method for non-effectively grounded distribution systems is presented in this paper.A special diagnostic signal current is injected into the fault distribution system,and then it is detected at the outlet terminals to identify the fault line and at the sectionalizing or branching point along the fault line to locate the fault section.The method has been put into application in actual distribution network and field experience shows that it can identify the fault line and locate the fault section correctly and effectively.
文摘A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.
文摘In this paper, we suggest a novel parsimonious neurofuzzy model realized by RBFNs for railway carriage system identification and fault diagnosis. To overcome the curse of dimensionality resulting from high dimensional input variables, in our developed model the features extracted from the available observations are regarded as the input variables by adopting the higher-order statistics(HOS) technique. Such a constructed model is also applied to a practical railway carriage system, simulation results indicate that the developed neurofuzzy model possesses strong identification and fault diagnosis ability.
基金Project supported by the Major Technological Project of PetroChina“Research and Application of Key Geophysical Techniques for Carbonate Rocks in Sichuan Basin”(No.:2016E-0604)the PetroChina-SWPU Technological Cooperation Project“Research on Basic Theory and Key Technology of Exploration and Development of Deep/Ultra-deep Carbonate Gas Reservoirs”(No.:2020CX010000)。
文摘The Sichuan Basin has abundant oil and gas resources and it has been generally acknowledged as a super petroliferous basin.In recent years,immense exploration and development potential of deep natural gas emerges in the Sichuan Basin.The identification of strikeeslip faults and their reservoir controlling effects are of great significance to develop a new exploration domain of fault-controlled gas reservoirs.In order to clarify the distribution of strikeeslip faults in the Sichuan Basin and their relationships with natural gas exploration,in view that the strikeeslip faults in this basin are small and complex with poor seismic data and influenced more by the fold deformation of Triassic gypsum salt beds,this paper identifies strikeeslip faults and analyzes their characteristics,distribution and genesis based on the interpretation of continuous 3D seismic data of the central Sichuan Basin.Then,the fault-controlled gas reservoir model of strike-slip fault is established by reviewing old wells and evaluating geological conditions of hydrocarbon accumulation.Finally,the exploration potential and exploration direction of fault-controlled gas reservoirs are discussed.And the following research results are obtained.First,intraplate weak strikeeslip faults can be identified by combining the seismic profile marker of strikeeslip fault with the plane marker.Second,eight large strikeeslip fault zones with a total length of 1280 km are identified and confirmed in the central Sichuan Basin.Third,strikeeslip faults are mainly distributed in the SinianePermian System,mainly composed of small-displacement oblique faults and echelon faults,and different in stratification,grading,classification and segmentation.Fourth,under the control of the oblique subduction of the Proto-Tethys Ocean and the different structures of the basement,the strikeeslip faults in the central Sichuan Basin were formed in the Sinian and there are multiple stages of inherited CaledonianeHercynian activities.Fifth,the strikeeslip faults in the central Sichuan Basin play a role in sourceereservoir connection,reservoir stimulation,and gas accumulation and high yield,and constitute the fault-controlled natural gas accumulation system of“three-element controlling reservoir and composite hydrocarbon accumulation”based on source rock,fault and reservoir.In conclusion,a large intraplate strikeeslip fault system is developed in the Sichuan Basin,which has the geological conditions of hydrocarbon accumulation to form strikeeslip fault-controlled large gas field,and the large source connecting strike-slip fault zones are the main breakthrough directions of natural gas exploration in fault-controlled carbonate rocks.
文摘Nowadays, the DC distribution system has been suggested, as a replacement for the AC power distribution system with electric propulsion. This idea signifies a fresh approach of issuing energy for low-voltage installations. It can be used for any electrical application up to 20 MW and works at a nominal voltage of 1000 V DC. The DC distribution system is just an extension of the multiple DC links that previously available in all propulsion and thruster drives, which typically comprise more than 80% of the electrical power consumption on electric propulsion vessels. A fault detection and islanding scheme for DC grid connected PV system is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of this paper are to classify and detect the fault in the PV system as well as DC grid and to isolate the faulted section so that the system keeps operating without disabling the entire system. The results show the measured values of power at PV panel and DC grid side under different fault condition, which indicates the type of fault that occurs in the system.
基金supported by the Spanish Ministry of Science and Innovation under the MCI/AEI/FEDER project number PID2021-123543OBC21.
文摘Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency,reduce maintenance costs,extend their lifespan,and enhance reliability in the wind energy sector.This is particularly necessary in offshore wind,currently one of the most critical assets for achieving sustainable energy generation goals,due to the harsh marine environment and the difficulty of maintenance tasks.To address this problem,this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines,using normalized and linearized operational data.The proposed framework transforms heterogeneous wind speed and power measurements into a unified scale,enabling the development of a new wind power index(WPi)that quantifies deviations from expected performance.Additionally,spatial and temporal coherence analyses of turbines within a wind farm ensure the validity of these normalized measurements across different wind turbine models and operating conditions.Furthermore,a Support Vector Machine(SVM)refines the classification process,effectively distinguishing measurement errors from actual power generation failures.Validation of this strategy using real-world data from the Alpha Ventus wind farm demonstrates that the proposed approach not only improves predictive maintenance but also optimizes energy production,highlighting its potential for broad application in offshore wind installations.
