Remaining Useful Life Estimation of Bearing Using Deep Multiscale Window-Based Transformer

Abstract

Accurate and reliable remaining useful life (RUL) estimation for bearings relies on the extraction of desired degradation features from vibration signals. Now, various feature extractors based on convolutional neural networks have been proposed for RUL prediction and achieved great success. However, these approaches focus on extracting degradation features from data in a single time frame but fail to capture the degradation information across multiple time frames, leading to degenerate estimation performance. In this study, we propose a novel multi-scale feature extractor called deep multi-scale window-based transformer (DMW-Trans) to overcome these limitations. To be more specific, a bunch of feature maps (FMs) are firstly generated with time-frequency maps (TFMs) at multiple time frames. Then, sequential layers equipped with window-based transformer blocks, i.e., WT-Blocks, and patch-merge modules are designed, where the former is used to extract features with multi-head self-attention windows while the latter is employed to fuse features from neighboring areas on TFMs to enlarge the attention fields from layer to layer. Through this hierarchical feature extraction process, multi-scale degradation related features can be effectively extracted. Lastly, multi-layer perceptron is applied to fuse the multi-scale features to estimate RUL. Due to the excellent multi-scale feature extraction capability, the proposed DMW-Trans can provide accurate and reliable RUL prediction results. Extensive experiments on two public run-to-failure bearing datasets demonstrate the superior performance of our proposed method compared to some state-of-the-art approaches.