Development of Feature Tokenizer Deep Learning Model for Fault Diagnosis in Marine Propulsion System
The fault diagnostics in Brushless Direct Current (BLDC) motor drive system is critical for operational safety and system lifespan in propulsion system applications. However, signature parameters such as currents, voltages, speed, and torque have provided nonlinear behavior, which limits the usefulness of traditional model-based approaches. This research provides a deep learning based intelligent system to monitor the failures in marine propulsion system. Each signal feature is represented as a structured token, with a specific class token used to collect global contextual information. The proposed model captures both local temporal dynamics and global inter-feature interdependence multi-layer self-attention processes, allowing for the thorough modeling of complex fault patterns. The framework is tested on datasets including healthy conditions and fault conditions, such as motor faults and drive switch failures. The impact of signal-to-noise ratio in the different case structure on the various signals are investigated. With clean signal conditions (SNR = 100 dB), the proposed model achieves a motor fault classification accuracy of 89%, with a weighted precision of 0.91, recall of 0.89, and F1-score of 0.89 on the hardware testbed. Drive switch fault classification under clean simulation conditions achieves an accuracy of 97%, with a macro-averaged F1-score of 0.98 including perfect classification (F $1=1.0$ ) for four out of six switch fault types. Robustness evaluation under additive white Gaussian noise reveals motor fault accuracies of 89%, 80%, and 27% at SNR levels of 100 dB, 50 dB, and 10 dB respectively, and drive fault accuracies of 93%, 51%, and 56% at the corresponding SNR levels. Compared to CNN and LSTM based baselines, the proposed model improves diagnostic accuracy by (4–8%) with a significantly reduced false positive rate, confirming its suitability for intelligent real-time fault diagnosis in marine propulsion systems.