A Novel Approach for Sensitive Inter-turn Fault Detection in Induction Motor Under Various Operating Conditions

Sonje, Deepak M.; Kundu, Prasanta; Chowdhury, Anandita

doi:10.1007/s13369-018-03690-w

Cited by 25 publications

(9 citation statements)

References 25 publications

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“…Energies 2022, 15, x FOR PEER REVIEW 2 of 17 fault's further expansion and significantly reduce the maintenance cost. Since the ITSC fault of the asynchronous traction motor is more hidden than the main insulation system fault, it is more difficult to detect the incipient ITSC fault [15,16].…”

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

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show abstract

“…Hence, several harmonic components are generated in the current that are dependable on the loading condition of the motor. These harmonics are expressed in equation (1) (Almounajjed et al, 2021b; Baruti et al, 2020; Sonje et al, 2019) as…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

Almounajjed¹,

Sahoo²

2022

Transactions of the Institute of Measurement and Control

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This work proposes a novel online detection scheme to diagnose incipient inter-turn short circuit fault and estimate the failure severity in induction motor. Incipient detection of the stator failure during the machine running, as well as identification of its intensity can reduce the risk of additional damage to the phase winding, improve the operational efficiency, and ensure machine availability. Hence, the incipient fault diagnosis provides a safe operating area for the motor. This work aims to specify the percentage of defective turns in the shorted winding by proposing a new mathematical parameter based on wavelet analysis, in addition to employ a multi-class support vector machine to perform the classification task. Discrete Wavelet Transform is used to analyze the stator currents after modeling the motor utilizing Clarke-Concordia transformation. From the detailed coefficients, Max and L2 norms are calculated. The adopted parameter is computed depending on the previous norms, which form the input vector to feed the classifier. The multi-class support vector machine–based one versus one algorithm is used to determine accurately the defect intensity. The acquired outcomes prove that the proposed approach, depending on the novel parameter along with multi-class support vector machine can give a robust and accurate indication about the machine status, which enables the estimation of the fault severity. To verify the competency of the methodology, various hardware experiments are carried out on the motor. The experimental results demonstrate the validity and practicability of the method, with a higher level of correctness, exceeding 96%.

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“…Therefore, fault detection and diagnosis based on electrical signals is a good choice for MCTs. There are many fault detection studies where power signals or modular square signals are used to detect faults [25,26]. However, the power signal is calculated from the voltage and current signal, and the mode square signal is obtained by the three-phase electrical signal.…”

Section: Introductionmentioning

confidence: 99%

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Wei

Xie

Shi

et al. 2021

JMSE

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Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

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A Novel Approach for Sensitive Inter-turn Fault Detection in Induction Motor Under Various Operating Conditions

Cited by 25 publications

References 25 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

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

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