Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To...Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis areused in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.展开更多
Purpose–The electromechanical brake system is leading the latest development trend in railway braking technology.The tolerance stack-up generated during the assembly and production process catalyzes the slight geomet...Purpose–The electromechanical brake system is leading the latest development trend in railway braking technology.The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder.The tolerance leads to imprecise brake control,so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system.This paper aims to present improved variational mode decomposition(VMD)algorithm,which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approach–The VMD algorithm plays a pivotal role in the preliminary phase,employing mode decomposition techniques to decompose the motor speed signals.Afterward,the error energy algorithm precision is utilized to extract abnormal features,leveraging the practical intrinsic mode functions,eliminating extraneous noise and enhancing the signal’s fidelity.This refined signal then becomes the basis for fault analysis.In the analytical step,the cepstrum is employed to calculate the formant and envelope of the reconstructed signal.By scrutinizing the formant and envelope,the fault point within the electromechanical brake system is precisely identified,contributing to a sophisticated and accurate fault diagnosis.Findings–This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake(EMB)motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction.The signal is reconstructed according to the effective intrinsic mode functions(IMFS)component of removing noise,and the formant and envelope are calculated by cepstrum to locate the fault point.Experiments show that the empirical mode decomposition(EMD)algorithm can effectively decompose the original speed signal.After feature extraction,signal enhancement and fault identification,the motor mechanical fault point can be accurately located.This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/value–By using this improved VMD algorithm,the electromechanical brake system can precisely identify the rotational anomaly of the motor.This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled.Compared with the conventional motor diagnosis method,this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs.Moreover,the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.展开更多
文摘Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis areused in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.
基金funded by the Science Foundation of China Academy of Railway Science,grant number 2020YJ175.
文摘Purpose–The electromechanical brake system is leading the latest development trend in railway braking technology.The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder.The tolerance leads to imprecise brake control,so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system.This paper aims to present improved variational mode decomposition(VMD)algorithm,which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approach–The VMD algorithm plays a pivotal role in the preliminary phase,employing mode decomposition techniques to decompose the motor speed signals.Afterward,the error energy algorithm precision is utilized to extract abnormal features,leveraging the practical intrinsic mode functions,eliminating extraneous noise and enhancing the signal’s fidelity.This refined signal then becomes the basis for fault analysis.In the analytical step,the cepstrum is employed to calculate the formant and envelope of the reconstructed signal.By scrutinizing the formant and envelope,the fault point within the electromechanical brake system is precisely identified,contributing to a sophisticated and accurate fault diagnosis.Findings–This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake(EMB)motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction.The signal is reconstructed according to the effective intrinsic mode functions(IMFS)component of removing noise,and the formant and envelope are calculated by cepstrum to locate the fault point.Experiments show that the empirical mode decomposition(EMD)algorithm can effectively decompose the original speed signal.After feature extraction,signal enhancement and fault identification,the motor mechanical fault point can be accurately located.This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/value–By using this improved VMD algorithm,the electromechanical brake system can precisely identify the rotational anomaly of the motor.This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled.Compared with the conventional motor diagnosis method,this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs.Moreover,the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.
