A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation

Mejia-Barron, Arturo; Tapia-Tinoco, Guillermo; Razo-Hernández, José R.; Valtierra-Rodríguez, Martín; Granados-Lieberman, David

doi:10.1016/j.compeleceng.2021.107234

Cited by 30 publications

(19 citation statements)

References 20 publications

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“…The ITSC fault diagnosis of three-phase asynchronous motors mainly includes model-based, signal process-based, and artificial intelligence-based diagnosis methods [17][18][19]. An accurate motor ITSC fault model is needed for model-based ITSC fault diagnosis.…”

Section: Introduction 27mentioning

confidence: 99%

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on ApFFT Time-shift Phase Difference Spectrum Correction and SVM

Ma¹,

Liu²,

Hu³

et al. 2023

Preprint

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The EMU(electric multiple units) traction motors are powered by converters. The PWM(pulse width modulation) voltage increases the voltage stress borne by the motor insulation system, making the ITSC(inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power was proposed in the article to evaluate the non-metallic ITSC faults degree. The apFFT(all phase FFT) time-shift phase difference correction with double Hanning-windows is used to calculate the fundamental frequency of the traction motor's ZSVC（zero se-quence voltage component）, the fundamental amplitudes of ZSVC and three-phase current. The five parameters are used as fault features to train the SVM (support vector machine)fault diagnosis model. The SVM hyper-parameters C and g are optimized by K-CV (K fold cross-validation) and grid search methods. The experimental verification was carried out by the EMU electric traction simulation experimental platform. According to the non-metallic degree index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training data set and 93.33 % on the test data set. There was no misclassification between normal and serious ITSC fault samples.

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Section: Introduction 27mentioning

confidence: 99%

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on ApFFT Time-shift Phase Difference Spectrum Correction and SVM

Ma¹,

Liu²,

Hu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These techniques could be intrusive, which require installing some additional sensors, or nonintrusive using the already installed sensors [15]. From the considered signals, the signature-based approaches can be classified into thermal [13], acoustic [14], flux [12], impedance [11], voltage [10], power [16], and current techniques [4,[17][18][19][20][21]. Among them, the methods based on motor current signal analysis (MCSA) have been more reported.…”

Section: Introductionmentioning

confidence: 99%

Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors

Ghanbari,

Mehraban

2023

IET Electric Power Appl

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An efficient method for the detection of the stator winding faults (SWFs) of induction motors was dealt based on Park's vector of rotor slot harmonics (RSHs). The induced harmonic voltages of the RSHs in the stator windings are the supreme signatures for fault detection, which can be monitored in the motor currents. Using such a signature, unbalanced supply voltage and stator windings fault situations could be segregated straightforwardly. On the other hand, despite some merits, Park's vector analysis‐based methods suffer from low sensitivity and unreliability in the case of low‐severity faults. To have the advantages of both methods while tackling the shortcomings, Park's vector approach and the RSH signatures are combined in the proposed method. The RSHs are extracted using the fast Fourier transform method and the least squared approach is employed for pattern recognition. Different experimental results under various scenarios, even in the case of low severity faults, confirm that the locus of Park's vector is an elliptical shape in SWFs, while it is shapeless in other cases, such as healthy and unbalanced supply voltage cases.

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Section: Introductionmentioning

confidence: 99%

“…The trained RBF neural network database is established by using chaotic differential evolution algorithm to optimize the gain scheduling Fractional Order PID under multiple wind speeds. 28 In addition, there are some researches on the control of relevant motor load system by using the characteristics of RBF neural network, which can be referred to Lei et al, 29 Wu et al, 30 Mejia-Barron et al, 31 Cherif et al, 32 and C xetin et al 33 RBF neural network can approximate any continuous function, and it adopts local approximation method, which can achieve good results in non-linear system, and is suitable for real-time control with a fastlearning speed. The PID controller has simple structure and strong adaptability, but its nonlinear ability is weak.…”

Section: Introductionmentioning

confidence: 99%

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

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A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation

Cited by 30 publications

References 20 publications

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on ApFFT Time-shift Phase Difference Spectrum Correction and SVM

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on ApFFT Time-shift Phase Difference Spectrum Correction and SVM

Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

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