An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning
Early detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent. This paper introduces a Weighted Probability Ensemble Deep L...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11037405/ |
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| author | Usman Ali Umer Ramzan Waqas Ali Khaled Ali Al-Jaafari |
| author_facet | Usman Ali Umer Ramzan Waqas Ali Khaled Ali Al-Jaafari |
| author_sort | Usman Ali |
| collection | DOAJ |
| description | Early detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent. This paper introduces a Weighted Probability Ensemble Deep Learning (WPEDL) methodology, tailored for effectively diagnosing induction motor faults using high-dimensional data extracted from vibration and current features. The Short-Time Fourier Transform (STFT) extracts features from vibration and current signals. The performance of the WPEDL fault diagnosis method is compared against conventional deep learning models, demonstrating the superior efficacy of the proposed system. The multi-class fault diagnosis system based on WPEDL achieves high accuracies across different fault types: 99.05% for bearing (vibrational signal), 99.10%, and 99.50% for rotor (current and vibration signal), and 99.60%, and 99.52% for stator faults (current and vibration signal) respectively. To evaluate the robustness of our multi-class classification decisions, tests have been conducted on a combined dataset of 52,000 STFT images encompassing all three faults. Our proposed model outperforms other models, achieving an accuracy of 98.89%. The findings underscore the effectiveness and reliability of the WPEDL approach for early-stage fault diagnosis in IMs, offering promising insights for enhancing industrial operational efficiency and reliability. |
| format | Article |
| id | doaj-art-0615862f442a472490b838bcedca768f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0615862f442a472490b838bcedca768f2025-08-20T02:20:40ZengIEEEIEEE Access2169-35362025-01-011310695810697310.1109/ACCESS.2025.358015911037405An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep LearningUsman Ali0https://orcid.org/0009-0000-4216-2823Umer Ramzan1https://orcid.org/0009-0002-2760-9724Waqas Ali2https://orcid.org/0000-0003-0066-4256Khaled Ali Al-Jaafari3https://orcid.org/0000-0003-1990-5486School of Engineering and Applied Sciences, GIFT University, Gujranwala, PakistanSchool of Engineering and Applied Sciences, GIFT University, Gujranwala, PakistanDepartment of Electrical Engineering (RCET), University of Engineering and Technology, Lahore, PakistanDepartment of Electrical Engineering, Advanced Power and Energy Center, Khalifa University, Abu Dhabi, United Arab EmiratesEarly detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent. This paper introduces a Weighted Probability Ensemble Deep Learning (WPEDL) methodology, tailored for effectively diagnosing induction motor faults using high-dimensional data extracted from vibration and current features. The Short-Time Fourier Transform (STFT) extracts features from vibration and current signals. The performance of the WPEDL fault diagnosis method is compared against conventional deep learning models, demonstrating the superior efficacy of the proposed system. The multi-class fault diagnosis system based on WPEDL achieves high accuracies across different fault types: 99.05% for bearing (vibrational signal), 99.10%, and 99.50% for rotor (current and vibration signal), and 99.60%, and 99.52% for stator faults (current and vibration signal) respectively. To evaluate the robustness of our multi-class classification decisions, tests have been conducted on a combined dataset of 52,000 STFT images encompassing all three faults. Our proposed model outperforms other models, achieving an accuracy of 98.89%. The findings underscore the effectiveness and reliability of the WPEDL approach for early-stage fault diagnosis in IMs, offering promising insights for enhancing industrial operational efficiency and reliability.https://ieeexplore.ieee.org/document/11037405/Induction motorensemble learningdeep learningSTFT imagesfault diagnosis |
| spellingShingle | Usman Ali Umer Ramzan Waqas Ali Khaled Ali Al-Jaafari An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning IEEE Access Induction motor ensemble learning deep learning STFT images fault diagnosis |
| title | An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning |
| title_full | An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning |
| title_fullStr | An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning |
| title_full_unstemmed | An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning |
| title_short | An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning |
| title_sort | improved fault diagnosis strategy for induction motors using weighted probability ensemble deep learning |
| topic | Induction motor ensemble learning deep learning STFT images fault diagnosis |
| url | https://ieeexplore.ieee.org/document/11037405/ |
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