Bearing RUL Prediction and Fault Diagnosis System based on Parallel Multi-scale MIMT Lightweight Model

Abstract In actual industrial production, the importance of safety production is increasingly prominent, and the degradation and failure of machinery and equipment are potential sources of safety hazards. Therefore, there is a growing trend towards real-time monitoring, prediction, and diagnosis of industrial equipment to prevent unpredictable impacts on life and property safety caused by sudden failures. To address this issue, this paper proposes a real-time degradation anomaly detection based on parallel multiscale autoencoders and a lightweight model of parallel multiscale multi-input multi-task for bearing Remaining Useful Life (RUL) prediction and fault diagnosis systems. Firstly, the multiscale autoencoder method is used to simulate actual working conditions and reconstruct the original vibration signals to build abnormal degradation detection intervals. The [0, $\mu$ +3$\sigma$] interval is utilized to judge abnormal degradation based on reconstruction errors, and the First Predict Timepoint (FPT) is determined adaptively. Secondly, a method for constructing dimensionless auxiliary datasets is proposed, which adopts a multi-input form based on deep separable convolution for feature extraction of original vibration signals, kurtosis, and peak values to improve the prediction and diagnosis performance of the lightweight model. Finally, a multi-task output method combining clustering and regression is employed to achieve RUL prediction and fault diagnosis of bearings. The proposed method overcomes the problems existing in traditional bearing RUL prediction and diagnosis methods and possesses theoretical innovation and engineering practicality. Validation on two bearing datasets confirms the effectiveness of the proposed method.