Paper
19 September 2024 Onboard fault diagnosis of incipient stator turn-turn failure for industrial machines
Amar Kumar Verma, Afroz A. Saad, Anurag Choudhary, S. Fatima, B. K. Panigrahi
Author Affiliations +
Proceedings Volume 13225, Sixth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2024); 1322507 (2024) https://doi.org/10.1117/12.3046499
Event: Sixth International Conference on Image, Video Processing and Artificial Intelligence, 2024, Kuala Lumpur, Malaysia
Abstract
This paper investigates the severity of stator winding insulation failures by combining deep learning with time-frequency-based features. These time-based features include rms, peak, skew, crest, and kurtosis, while frequency-based features dominant frequency and wavelet energy. The initial phase of stator winding insulation degradation is identified as a Stator Turn-Turn Fault (STTF). It is critical to monitor industrial machines to prevent catastrophic failures. This work develops an experimental test-rig setup that mimics various STTFs to imitate real-time industry conditions. The proposed time-frequency-based deep learning model achieved 96.4% accuracy using raw experimental data to identify the six fault conditions, including two early stages, two intermittent stages, and two severity stages within the same winding phase from a squirrel cage induction motor while achieving 100% on featured data. The robustness of the proposed model was validated using unseen data from a different machine with unknown STTF conditions with an accuracy of 91.78%. This implies the reliability and scalability of the model, which can be adapted for industry use.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Amar Kumar Verma, Afroz A. Saad, Anurag Choudhary, S. Fatima, and B. K. Panigrahi "Onboard fault diagnosis of incipient stator turn-turn failure for industrial machines", Proc. SPIE 13225, Sixth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2024), 1322507 (19 September 2024); https://doi.org/10.1117/12.3046499
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KEYWORDS
Data modeling

Failure analysis

Performance modeling

Deep learning

Diagnostics

Feature extraction

Machine learning

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