A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

Many kinds of electrical equipment are used in civil and building engineering.The motor is one of the main power components of this electrical equipment,which can provide stable power output.During the long-term use of motors,various motor faults may occur,which affects the normal use of electrical equipment and even causes accidents.It is significant to apply fault diagnosis for the motors at the construction site.Aiming at the problem that signal data of faulty motor lack diversity,this research designs a multi-layer perceptron Wasserstein generative adversarial network,which is used to enhance training data through distribution fusion.A discrete wavelet decomposition algorithm is employed to extract the low-frequency wavelet coefficients from the original motor current signals.These are used to train themulti-layer perceptron Wasserstein generative adversarial model.Then,the trainedmodel is applied to generate fake current wavelet coefficients with the fused distribution.A motor fault classification model consisting of a feature extractor and pattern recognizer is built based on perceptron.The data augmentation experiment shows that the fake dataset has a larger distribution than the real dataset.The classification model trained on a real dataset,fake dataset and combined dataset achieves 21.5%,87.2%,and 90.1%prediction accuracy on the unseen real data,respectively.The results indicate that the proposed data augmentation method can effectively generate fake data with the fused distribution.The motor fault classification model trained on a fake dataset has better generalization performance than that trained on a real dataset.

Research on motor rotation anomaly detection based on improved VMD algorithm

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.

Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.