Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis

Almounajjed, Abdelelah; Sahoo, Ashwin Kumar; Kumar, Mani Kant

doi:10.1016/j.measurement.2021.109780

Cited by 56 publications

(32 citation statements)

References 31 publications

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“…Most studies focus on current analysis to detect inter turns short circuit fault in the stator winding. In [16], discrete wavelet transform is designed to analyse the currents to detect the ITSC in the first time and estimate the number of defective turns in faulty phase. FFT and fuzzy logic are applied in [17] based on the stator currents to detect the shorted phase winding.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis

Ouamara¹,

Boukhnifer²,

Chaibet³

et al. 2023

Diagnostyka

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The three-phase induction motor is well suited for a wide range of mobile drives, specifically for electric vehicle powertrain. During the entire life cycle of the electric motor, some types of failures can occur, with stator winding failure being the most common. The impact of this failure must be considered from the incipient as it can affect the performance of the motor, especially for electrically powered vehicle application. In this paper, the intern turn short circuit of the stator winding was studied using Fast Fourier transform (FFT) and Shor-Time Fourier transform (STFT) approaches. The residuals current between the estimated currents provided by the extended Kalman filter (EKF) and the actual ones are used for fault diagnosis and identification. Through FFT, the residual spectrum is sensitive to faults and gives the extraction of inter-turn short circuit (ITSC) related frequencies in the phase winding. In addition, the FFT is used to obtain information about when and where the ITSC appears in the phase winding. Indeed, the results allow to know the faulty phase, to estimate the fault rate and the fault occurrence frequency as well as their appearance time.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis

Ouamara¹,

Boukhnifer²,

Chaibet³

et al. 2023

Diagnostyka

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“…The residual harmonics are measured by means of the linear fast Fourier transform spectrum (LFFTS) of the healthy motor current signal and the faulty condition. In another example, the research presented in [11] calculates a fault intensity index in induction motors for the inter-turn short circuit fault of the stator winding. For this purpose, the raw current signal in the time domain obtained from the motor is processed through the discrete wavelet transform (DWT), and by using the detailed coefficients, the statistical parameter of the maximum norm is computed under several load conditions and fault severities.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effects Produced by Pure Sine and Modified Sine Inverters in an Induction Motor

Jaen-Cuéllar¹,

Elvira-Ortiz²,

Resendiz-Ochoa³

et al. 2023

New Trends in Electric Machines - Technology and Applications

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Most of the industrial applications are supported by complex machinery, which in turn are supported by electrical motors to perform specific tasks in multiple processes. Certainly, induction motors are the most widely used electrical machines in a majority of industrial machineries; in this sense, their operating condition plays an important role to ensure the machinery availability and to avoid unwanted stoppages. On the other hand, several sources may lead to producing faults in induction motors, but most of the common faults are produced by electrical or mechanical stresses, where the mechanical stresses are usually produced by unbalances or misalignments and the electrical stresses are generated by fluctuations or variations in the power supply. Thereby, when the induction motors are fed through inverters due to renewable energy, their operation may present slight variations since the sine wave has no perfect generation. In this regard, this work presents an analysis of the effects produced by pure sine and modified sine inverters in an induction motor. Such analysis consists of studying the characteristic patterns, reflected as percentage variations in some metrics, such as ranges, rms values, and harmonic distortion, that induction motors produce over vibration signals, electrical signals (stator current and fed voltages), and rotating speed.

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“…Due to its low maintenance cost, high robustness, and required characteristics for a lot of industrial applications, the induction motor (IM) currently controls the domain of electromechanical energy conversion systems (Almounajjed et al, 2021a; Mayadevi et al, 2020). The IM faults diagnosis at an early stage can reduce breakdowns and minimize maintenance time (Almounajjed et al, 2022).…”

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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Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis

Cited by 56 publications

References 31 publications

Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis

Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis

Analysis of the Effects Produced by Pure Sine and Modified Sine Inverters in an Induction Motor

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

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