A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations
<p dir="ltr">Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, par...
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2025
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| _version_ | 1864513533214982144 |
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| author | Muhammad Irfan Ishaq (22564652) |
| author2 | Muhammad Adnan (678952) Muhammad Ali Akbar (16875915) Amine Bermak (1895947) Nimra Saeed (22564655) Maaz Ansar (22564658) |
| author2_role | author author author author author |
| author_facet | Muhammad Irfan Ishaq (22564652) Muhammad Adnan (678952) Muhammad Ali Akbar (16875915) Amine Bermak (1895947) Nimra Saeed (22564655) Maaz Ansar (22564658) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Irfan Ishaq (22564652) Muhammad Adnan (678952) Muhammad Ali Akbar (16875915) Amine Bermak (1895947) Nimra Saeed (22564655) Maaz Ansar (22564658) |
| dc.date.none.fl_str_mv | 2025-06-19T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3574017 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Hybrid_AI_Approach_for_Fault_Detection_in_Induction_Motors_Under_Dynamic_Speed_and_Load_Operations/30540710 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Manufacturing engineering Information and computing sciences Machine learning Induction motor fault diagnosis acoustic signals vibrational signals CNN-LSTM electro-mechanical faults Accuracy Convolutional neural networks Motors Transforms Induction motors Feature extraction Vibrations Rotors Continuous wavelet transforms |
| dc.title.none.fl_str_mv | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. To resolve this issue, this paper presents a unique hybrid Convolutional Neural Network (CNN) along with the Long Short Term Memory (LSTM) topology for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. The proposed method can identify faults such as rotor imbalances, misalignment, stator winding issues, voltage imbalances, broken rotor bars, and broken bearings. Experiments performed using the University of Ottawa Electric Motor Dataset – Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) dataset reveals that all three accelerometers are 99.93% accurate at constant speed and 99.96% at variable speed under both loaded and unloaded conditions. In terms of fault diagnostic accuracy in an IM operating at different speeds and load conditions, this methodology outperforms cutting-edge methodologies in the literature. Moreover, using the publicly available CWRU dataset, this study validates the robustness of the proposed methodology in terms of operational issues in an IM. Finally, the proposed method achieves incredible results at varying speeds, stressing the need to improve industrial equipment reliability and maintenance methods.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3574017" target="_blank">https://dx.doi.org/10.1109/access.2025.3574017</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7fb94e09868bde995bdd1ed3545c0c08 |
| identifier_str_mv | 10.1109/access.2025.3574017 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30540710 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load OperationsMuhammad Irfan Ishaq (22564652)Muhammad Adnan (678952)Muhammad Ali Akbar (16875915)Amine Bermak (1895947)Nimra Saeed (22564655)Maaz Ansar (22564658)EngineeringElectrical engineeringManufacturing engineeringInformation and computing sciencesMachine learningInduction motorfault diagnosisacoustic signalsvibrational signalsCNN-LSTMelectro-mechanical faultsAccuracyConvolutional neural networksMotorsTransformsInduction motorsFeature extractionVibrationsRotorsContinuous wavelet transforms<p dir="ltr">Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. To resolve this issue, this paper presents a unique hybrid Convolutional Neural Network (CNN) along with the Long Short Term Memory (LSTM) topology for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. The proposed method can identify faults such as rotor imbalances, misalignment, stator winding issues, voltage imbalances, broken rotor bars, and broken bearings. Experiments performed using the University of Ottawa Electric Motor Dataset – Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) dataset reveals that all three accelerometers are 99.93% accurate at constant speed and 99.96% at variable speed under both loaded and unloaded conditions. In terms of fault diagnostic accuracy in an IM operating at different speeds and load conditions, this methodology outperforms cutting-edge methodologies in the literature. Moreover, using the publicly available CWRU dataset, this study validates the robustness of the proposed methodology in terms of operational issues in an IM. Finally, the proposed method achieves incredible results at varying speeds, stressing the need to improve industrial equipment reliability and maintenance methods.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3574017" target="_blank">https://dx.doi.org/10.1109/access.2025.3574017</a></p>2025-06-19T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3574017https://figshare.com/articles/journal_contribution/A_Hybrid_AI_Approach_for_Fault_Detection_in_Induction_Motors_Under_Dynamic_Speed_and_Load_Operations/30540710CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305407102025-06-19T12:00:00Z |
| spellingShingle | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations Muhammad Irfan Ishaq (22564652) Engineering Electrical engineering Manufacturing engineering Information and computing sciences Machine learning Induction motor fault diagnosis acoustic signals vibrational signals CNN-LSTM electro-mechanical faults Accuracy Convolutional neural networks Motors Transforms Induction motors Feature extraction Vibrations Rotors Continuous wavelet transforms |
| status_str | publishedVersion |
| title | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| title_full | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| title_fullStr | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| title_full_unstemmed | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| title_short | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| title_sort | A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations |
| topic | Engineering Electrical engineering Manufacturing engineering Information and computing sciences Machine learning Induction motor fault diagnosis acoustic signals vibrational signals CNN-LSTM electro-mechanical faults Accuracy Convolutional neural networks Motors Transforms Induction motors Feature extraction Vibrations Rotors Continuous wavelet transforms |