Attention-enhanced deep temporal fusion network for cloud-IIoT prognostics of EV induction motors under dynamic loads

The need for sophisticated and dependable prognostic systems for Induction Motors (IM) working under dynamic load and speed circumstances has increased due to the quick expansion of Electric Vehicles (EVs). Real-time defect prediction is limited by traditional multi-sensor data fusion techniques like concatenation, weighted averaging, or rule-based fusion, which are unable to determine which sensor is more informative at any given time. Additionally, early deterioration signs are missed by traditional models because they are unable to capture long-range temporal correlations across heterogeneous data. Additionally, these models have vanishing-gradient problems, which cause little but significant changes to be overlooked. An innovative prognostic learning system for integrated defect detection and remaining useful life (RUL) prediction in electric vehicle induction motors is presented in this work. It is based on Temporal Attention Fusion Long Short-Term Memory (TAF-LSTM) architecture. The suggested method successfully combines multi-domain feature extraction, long-sequence modeling, and temporal attention mechanisms to capture contextual fluctuations, gradual degradation patterns, and transient fault signals across several sensor channels. 99.9% fault diagnosis accuracy is demonstrated by experimental validation utilizing multi-sensor operating data. Additionally, for RUL prediction, the model obtains a Mean Squared Error (MSE) of 0.25 and a Root Mean Squared Error (RMSE) of 0.50, demonstrating extremely precise prognostic performance. These findings demonstrate that the suggested TAF-LSTM architecture provides a real-time, scalable, and dependable motor health monitoring solution with substantial promise for next-generation predictive maintenance in electric vehicles.