Abstract
Efficient and accurate bearing fault diagnosis and prognostics are crucial for predictive maintenance in industrial settings. Deep learning approaches have significantly advanced the field of time series classification, bringing powerful models to the forefront of prognostic and health management. Despite these advancements, current methods, including conventional Transformer models, encounter specific limitations with computational efficiency and the effective processing of long sequences. Addressing these challenges, our research presents an optimized Transformer-based model tailored for surmounting these hurdles, enhancing efficiency and ensuring high performance. By eschewing the traditional decoder in favor of an encoder-only architecture, our model capitalizes on the encoder’s ability to distill contextual information from sensor data. Enhanced with random convolutional kernel transform (ROCKET) for its exceptional feature extraction in time sequences and refined through principal component analysis (PCA) for dimensionality reduction, the proposed approach significantly boosts model efficiency, markedly trimming training duration, while achieving an exemplary accuracy of 99.10% on the benchmark bearing dataset. The proven efficiency and outstanding performance of our model hold significant promise for real-time predictive maintenance, offering a powerful tool for prompt and accurate fault detection.
