Onboard fault diagnosis of incipient stator turn-turn failure for industrial machines

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.