A two-stage framework for predicting the remaining useful life of bearings

Abstract

The traditional prediction of remaining useful life (RUL) for bearings cannot be calculated in parallel and requires manual feature extraction and artificial label construction. Therefore, this article proposes a two-stage framework for predicting the RUL of bearings. In the first stage, an unsupervised approach using a temporal convolutional network (TCN) is employed to construct a health indicator (HI). This helps reduce human interference and the reliance on expert knowledge. In the second stage, a prediction framework based on a convolutional neural network (CNN)–transformer is developed to address the limitations of traditional neural networks, specifically their inability to perform parallel calculations and their low prediction accuracy. The life prediction framework primarily maps the complete life data of bearings onto the HI vector. Based on the HI constructed through TCN, the known HI is input into the CNN–transformer network, which sequentially predicts the remaining unknown HI. Finally, the effectiveness and superiority of the proposed method are verified using two bearing datasets, providing validation of its capabilities.