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Regular Paper | Open Access

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Abdelelah Almounajjed1( )Ashwin Kumar Sahoo1Mani Kant Kumar2Sanjeet Kumar Subudhi1
Electrical Engineering Department, C.V Raman Global University, Bhubaneswar 752054, India
Electronics and Communication Engineering Department, C.V Raman Global University, Bhubaneswar 752054, India
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Graphical Abstract

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding. Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters L1 and L2 norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

Abstract

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding. Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters L1 and L2 norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

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Chinese Journal of Electrical Engineering
Pages 142-157
Cite this article:
Almounajjed A, Sahoo AK, Kumar MK, et al. Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique. Chinese Journal of Electrical Engineering, 2023, 9(1): 142-157. https://doi.org/10.23919/CJEE.2023.000003

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Received: 19 October 2021
Revised: 27 December 2021
Accepted: 19 January 2022
Published: 31 March 2023
© 2023 China Machinery Industry Information Institute
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