Unsupervised Convolutional Transformer Autoencoder for Robust Health Indicator Construction and RUL Prediction in Rotating Machinery

Abstract

Prognostics for rotating machinery, particularly bearings, encounter significant challenges in constructing reliable health indicators (HIs) that accurately reflect degradation trajectories, thereby enabling precise remaining useful life (RUL) predictions. This article proposes a novel integrated approach for predicting the RUL of bearings without manual feature engineering. Specifically, a sequential autoencoder integrating a convolutional neural network (CNN) and vision Transformer (Vi-T) is employed to capture the local spatial patterns and global temporal correlations of time-domain vibration signals. The Wasserstein distance is introduced to quantify the divergence between healthy and degraded signal embeddings, resulting in a robust HI metric. Subsequently, the derived HI is fed into a CNN-bidirectional long short-term memory-regressor with Monte Carlo dropout to provide RUL predictions and Bayesian uncertainty estimates. Experimental results from the Xi’an Jiao-Tong University bearing dataset demonstrate that the proposed method surpasses conventional techniques in HI construction and RUL prediction accuracy, demonstrating its efficacy for complex industrial systems with minimal data preprocessing.