Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor
Abstract Early detection of incipient faults in three-phase induction motors is crucial to enhance system reliability and to minimize unplanned operational interruptions in industrial environments. Traditional diagnostic techniques often struggle to detect incipient faults, especially under fluctuat...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | Journal of Electrical Systems and Information Technology |
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| Online Access: | https://doi.org/10.1186/s43067-025-00244-7 |
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| author | Sudeep Samanta Jitendra Nath Bera Amitava Biswas |
| author_facet | Sudeep Samanta Jitendra Nath Bera Amitava Biswas |
| author_sort | Sudeep Samanta |
| collection | DOAJ |
| description | Abstract Early detection of incipient faults in three-phase induction motors is crucial to enhance system reliability and to minimize unplanned operational interruptions in industrial environments. Traditional diagnostic techniques often struggle to detect incipient faults, especially under fluctuating load conditions and may require complex signal processing or multiple sensors. The paper introduces a method for early detection of faults in three-phase induction motors using Wavelet Kernel-enabled convolutional neural networks (CNNs). The proposed system accurately identifies stator interturn faults in single or multiple phases and broken rotor bar faults, even under varying operating conditions such as load variations. By employing 14 mother wavelets as convolution filters, the method effectively extracts critical features from stator current signatures, streamlining the fault detection and classification process. This technique leverages the deep structures of CNNs to autonomously learn features from current signals, achieving a notable accuracy of above 97% in tests with both simulated model and two different hardware motor setup. The experimental result shows that it is capable of detecting as low as 1–2% of stator interturn fault with varying impedance in short circuit path as well as one broken rotor bar fault. Overall, the proposed method proves to be a powerful tool for the early diagnosis of incipient faults in induction motors with high degree of reliability and effectiveness. |
| format | Article |
| id | doaj-art-8a6f90c934bc428c98654d25cebb4fb6 |
| institution | Kabale University |
| issn | 2314-7172 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj-art-8a6f90c934bc428c98654d25cebb4fb62025-08-20T03:42:37ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722025-08-0112113410.1186/s43067-025-00244-7Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motorSudeep Samanta0Jitendra Nath Bera1Amitava Biswas2Department of Electrical Engineering, MCKV Institute of EngineeringDepartment of Applied Physics, University of CalcuttaDepartment of Applied Physics, University of CalcuttaAbstract Early detection of incipient faults in three-phase induction motors is crucial to enhance system reliability and to minimize unplanned operational interruptions in industrial environments. Traditional diagnostic techniques often struggle to detect incipient faults, especially under fluctuating load conditions and may require complex signal processing or multiple sensors. The paper introduces a method for early detection of faults in three-phase induction motors using Wavelet Kernel-enabled convolutional neural networks (CNNs). The proposed system accurately identifies stator interturn faults in single or multiple phases and broken rotor bar faults, even under varying operating conditions such as load variations. By employing 14 mother wavelets as convolution filters, the method effectively extracts critical features from stator current signatures, streamlining the fault detection and classification process. This technique leverages the deep structures of CNNs to autonomously learn features from current signals, achieving a notable accuracy of above 97% in tests with both simulated model and two different hardware motor setup. The experimental result shows that it is capable of detecting as low as 1–2% of stator interturn fault with varying impedance in short circuit path as well as one broken rotor bar fault. Overall, the proposed method proves to be a powerful tool for the early diagnosis of incipient faults in induction motors with high degree of reliability and effectiveness.https://doi.org/10.1186/s43067-025-00244-7Three phase induction motorFault diagnosisStator inter turn faultBroken rotor bar faultDeep learningWavelet kernel |
| spellingShingle | Sudeep Samanta Jitendra Nath Bera Amitava Biswas Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor Journal of Electrical Systems and Information Technology Three phase induction motor Fault diagnosis Stator inter turn fault Broken rotor bar fault Deep learning Wavelet kernel |
| title | Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor |
| title_full | Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor |
| title_fullStr | Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor |
| title_full_unstemmed | Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor |
| title_short | Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor |
| title_sort | wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor |
| topic | Three phase induction motor Fault diagnosis Stator inter turn fault Broken rotor bar fault Deep learning Wavelet kernel |
| url | https://doi.org/10.1186/s43067-025-00244-7 |
| work_keys_str_mv | AT sudeepsamanta waveletkernelandconvolutionneuralnetworkbasedaccuratedetectionofincipientstatorandrotorfaultsofinductionmotor AT jitendranathbera waveletkernelandconvolutionneuralnetworkbasedaccuratedetectionofincipientstatorandrotorfaultsofinductionmotor AT amitavabiswas waveletkernelandconvolutionneuralnetworkbasedaccuratedetectionofincipientstatorandrotorfaultsofinductionmotor |