Remaining useful life prediction of rolling bearings based on TCN-MSA

Abstract

Abstracts: As a pivotal element within the drive system of mechanical equipment, the Remaining Useful Life (RUL) of rolling bearings not only dictates the lifespan of the equipment's drive system but also the overall machine. An inaccurate prediction of the RUL of rolling bearings could hinder the formulation of maintenance strategies and lead to a chain of failures stemming from bearing malfunction, culminating in potentially catastrophic accidents. This paper designs a novel Temporal Convolutional Network-Multi-head Self-Attention (TCN-MSA) model for predicting the RUL of rolling bearings. This model considers the intricate non-linearity and complexity of mechanical equipment systems. It captures long-term dependencies using the causally inflated convolutional structure within the Temporal Convolutional Network (TCN) and simultaneously extracts features from the frequency domain signal. Subsequently, by employing the Multi-head Self-Attention (MSA) mechanism, the model discerns the significance of different features throughout the degradation process of rolling bearings by analyzing global information. The final prediction for rolling bearings' RUL has been successfully attained. To underline the excellence of the method presented in this paper, a comparative analysis was performed with existing methods, such as Convolutional Neural Network (CNN), Gate Recurrent Unit (GRU), and Temporal Convolutional Network (TCN). The results highlight that the model designed in this paper surpasses other existing methods in predicting the RUL of rolling bearings, demonstrating superior prediction accuracy and robust generalization capability.

A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

“…Thus, the practical and accurate prognostic and health management (PHM) of rolling bearings can grasp the health status in real-time for fault detection and remaining useful life (RUL) prediction, and it has received great attention nowadays [2][3][4]. In the study of PHM, RUL means the normal service life of the machinery before the occurrence of the failures [5]. Therefore, RUL prediction aims to guide the replacement strategies of rolling bearings to prevent sudden failures by predicting the health condition, which can ensure product safety, improve mechanical operation efficiency and reduce maintenance costs [6,7].…”

Bayesian Fusion of Degradation and Failure Time Data for Reliability Assessment of Industrial Equipment Considering Individual Differences

“…These models enable certain parameters within the stochastic process to conform to specific probability distributions, thereby allowing for a more adaptable depiction and the prediction of each equipment's degradation process. This approach more accurately captures individual differences [41,42]. Stochastic process models with random effects have gained widespread use in industrial reliability estimation for their efficiency in handling individual variability.…”