Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals,is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise f...Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals,is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise from the stator current signal that arises when rotor bars break. Then a Hilbert Transform is used to extract the envelope from the filtered signal. With the EMD method again,the frequency band containing the fault characteris-tic-frequency components,2sf,can be extracted from the signal's envelope. The last step is to use a Fast Fourier Trans-form (FFT) method to extract the fault characteristic frequency. This frequency can be detected in actual data from a faulty motor,as shown by example. Compared to the Extend Park Vector method this method is proved to be more sen-sitive under light motor load.展开更多
In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-...In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.展开更多
Induction motor faults including mechanical and electrical faults are reviewed.The fault diagnosis methods are summarized.To analyze the influence of stator current,torque,speed and rotor current on faulted bars,a tim...Induction motor faults including mechanical and electrical faults are reviewed.The fault diagnosis methods are summarized.To analyze the influence of stator current,torque,speed and rotor current on faulted bars,a time-stepping transient finite element(FE)model of induction motor with bars faulted is created in this paper.With wavelet package analysis method and FFT method, the simulation result of finite element is analyzed.Based on the simulation analysis,the on-line fault diagnosis system of induction motor with bars faulted is developed.With the speed of broken bars motor changed from 1 478 r/min to 1 445 r/min,the FFT power spectra and the wavelet package decoupling factors are given.The comparison result shows that the on-line diagnosis system can detect broken-bar fault efficiently.展开更多
基金Projects 50504015 supported by the National Natural Science Foundation of ChinaOC4499 by the Science Technology Foundation of China University ofMining & Technology
文摘Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals,is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise from the stator current signal that arises when rotor bars break. Then a Hilbert Transform is used to extract the envelope from the filtered signal. With the EMD method again,the frequency band containing the fault characteris-tic-frequency components,2sf,can be extracted from the signal's envelope. The last step is to use a Fast Fourier Trans-form (FFT) method to extract the fault characteristic frequency. This frequency can be detected in actual data from a faulty motor,as shown by example. Compared to the Extend Park Vector method this method is proved to be more sen-sitive under light motor load.
基金Projects(61490702,61773407,61803390,61751312)supported by the National Natural Science Foundation of ChinaProject(61725306)supported by the National Science Foundation for Distinguished Young Scholars of China+5 种基金Project(61621062)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of ChinaProject(2017TP1002)supported by Hunan Provincial Key Laboratory,ChinaProject(6141A0202210)supported by the Program of the Joint Pre-research Foundation of the Chinese Ministry of EducationProject(61400030501)supported by the General Program of the Equipment Pre-research Field Foundation of ChinaProject(2016TP1023)supported by the Science and Technology Project in Hunan Province Hunan Science and Technology Agency of ChinaProject(2018FJ34)supported by the Science and Technology Project in Shaoyang Science and Technology Agency of China
文摘In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.
文摘Induction motor faults including mechanical and electrical faults are reviewed.The fault diagnosis methods are summarized.To analyze the influence of stator current,torque,speed and rotor current on faulted bars,a time-stepping transient finite element(FE)model of induction motor with bars faulted is created in this paper.With wavelet package analysis method and FFT method, the simulation result of finite element is analyzed.Based on the simulation analysis,the on-line fault diagnosis system of induction motor with bars faulted is developed.With the speed of broken bars motor changed from 1 478 r/min to 1 445 r/min,the FFT power spectra and the wavelet package decoupling factors are given.The comparison result shows that the on-line diagnosis system can detect broken-bar fault efficiently.
