The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emergin...The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.展开更多
To improve the performance of the traditional fault-tolerant permanent magnet(PM)motor,the design and optimal schemes of dual-winding fault-tolerant permanent magnet motor(DWFT-PMM)are proposed and investigated.In ord...To improve the performance of the traditional fault-tolerant permanent magnet(PM)motor,the design and optimal schemes of dual-winding fault-tolerant permanent magnet motor(DWFT-PMM)are proposed and investigated.In order to obtain small cogging torque ripple and inhibiting the short-circuit current,the air gap surface shape of the PM and the anti short-circuits reactance parameters are designed and optimized.According to the actual design requirements of an aircraft electrical actuation system,the parameters,finite element analysis and experimental verification of the DWFT-PMM after optimal design are presented.The research results show that the optimized DWFT-PMM owns the merits of strong magnetic isolation,physics isolation,inhibiting the short circuit current,small cogging torque ripple and high fault tolerance.展开更多
Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure acc...Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.展开更多
In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural n...In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.展开更多
Winding short-circuit is one of the more common faults in switched reluctance motors(SRM).This paper takes an in-depth look at winding short-circuit.The characteristic of non-sinusoidal intermittent single phase curre...Winding short-circuit is one of the more common faults in switched reluctance motors(SRM).This paper takes an in-depth look at winding short-circuit.The characteristic of non-sinusoidal intermittent single phase current,fundamental components are extracted to reconstruct four phase symmetrical currents based on spectrum analysis of phase currents.The method of symmetrical component is used to calculate positive and negative sequence components of reconstructed currents,where then the ratio between positive and negative sequence component is seen as a fault feature and the diagnostic criterion is proposed.The simulation and experimental results are presented to confirm the implementation of the proposed method.展开更多
A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature ve...A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.展开更多
Induction motors are the most widespread rotating electrical machines in industry.Predictive maintenance of the motors is of crucial importance due to the fact that unexpected faults in those machines can lead to huge...Induction motors are the most widespread rotating electrical machines in industry.Predictive maintenance of the motors is of crucial importance due to the fact that unexpected faults in those machines can lead to huge economic losses for the corresponding companies.Over recent years,there is an increasing use of industrial induction motors operated by different types of drives,which have different functionalities.Among them,the use of soft-starters has proliferated due to the inherent benefits provided by these drives:they damp the high starting currents,enabling the soft startup of the motors and avoiding undesirable commutation transients introduced by other starting modalities.In spite of these advantages,they do not avoid the possible occurrence of rotor damages,one of the most common faults in this type of motors.Few works have proposed predictive maintenance techniques that are aimed to diagnose the rotor condition in soft-started machines and even fewer have demonstrated the validity of their methods in real motors.This work presents,for the first time,the massive validation of a rotor fault diagnosis methodology in soft-started induction motors.Industrial and laboratory and induction motors started under different types of soft-starters and with diverse rotor fault conditions are considered in the work.The results prove the potential of the approach for the reliable assessment of the rotor condition in such machines.展开更多
文摘The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.
基金This work was supported by the National Natural Science Foundation of China(51807094)the Fundamental Research Funds for the Central Universities(No.30918011327)and the Scientific Research Foundation of Nanjing University of Science and Technology(AE89991/036).
文摘To improve the performance of the traditional fault-tolerant permanent magnet(PM)motor,the design and optimal schemes of dual-winding fault-tolerant permanent magnet motor(DWFT-PMM)are proposed and investigated.In order to obtain small cogging torque ripple and inhibiting the short-circuit current,the air gap surface shape of the PM and the anti short-circuits reactance parameters are designed and optimized.According to the actual design requirements of an aircraft electrical actuation system,the parameters,finite element analysis and experimental verification of the DWFT-PMM after optimal design are presented.The research results show that the optimized DWFT-PMM owns the merits of strong magnetic isolation,physics isolation,inhibiting the short circuit current,small cogging torque ripple and high fault tolerance.
文摘Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.
文摘In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.
基金Supported by the Natural Science Foundation of Tianjin of China(No.16JCQNJC04200)the National Natural Science Foundation for nurturing of Tianjin University of Commerce(No.160123).
文摘Winding short-circuit is one of the more common faults in switched reluctance motors(SRM).This paper takes an in-depth look at winding short-circuit.The characteristic of non-sinusoidal intermittent single phase current,fundamental components are extracted to reconstruct four phase symmetrical currents based on spectrum analysis of phase currents.The method of symmetrical component is used to calculate positive and negative sequence components of reconstructed currents,where then the ratio between positive and negative sequence component is seen as a fault feature and the diagnostic criterion is proposed.The simulation and experimental results are presented to confirm the implementation of the proposed method.
基金Project supported by the Second Stage of Brain Korea 21 ProjectProject(2010-0020163) supported by Priority Research Centers Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education,Science and Technology
文摘A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.
基金Supported by the Spanish‘Ministerio de Economia y Competitividad’(MINECO)FEDER program in the framework of the‘Proyectos I+D del Subprograma de Generacion de Conocimiento,Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia’(ref:DPI2014-52842-P).”。
文摘Induction motors are the most widespread rotating electrical machines in industry.Predictive maintenance of the motors is of crucial importance due to the fact that unexpected faults in those machines can lead to huge economic losses for the corresponding companies.Over recent years,there is an increasing use of industrial induction motors operated by different types of drives,which have different functionalities.Among them,the use of soft-starters has proliferated due to the inherent benefits provided by these drives:they damp the high starting currents,enabling the soft startup of the motors and avoiding undesirable commutation transients introduced by other starting modalities.In spite of these advantages,they do not avoid the possible occurrence of rotor damages,one of the most common faults in this type of motors.Few works have proposed predictive maintenance techniques that are aimed to diagnose the rotor condition in soft-started machines and even fewer have demonstrated the validity of their methods in real motors.This work presents,for the first time,the massive validation of a rotor fault diagnosis methodology in soft-started induction motors.Industrial and laboratory and induction motors started under different types of soft-starters and with diverse rotor fault conditions are considered in the work.The results prove the potential of the approach for the reliable assessment of the rotor condition in such machines.