Remaining Useful Life Prediction Method Enhanced by Data Augmentation and Similarity Fusion

Abstract

Precise prediction of the remaining useful life (RUL) of rolling bearings is crucial for ensuring the smooth functioning of machinery and minimizing maintenance costs. The time-domain features can reflect the degenerative state of the bearings and reduce the impact of random noise present in the original signal, which is often used for life prediction. However, obtaining ideal training data for RUL prediction is challenging. Thus, this paper presents a bearing RUL prediction method based on unsupervised learning sample augmentation, establishes a VAE-GAN model, and expands the time-domain features that are calculated based on the original vibration signals. By combining the advantages of VAE and GAN in data generation, the generated data can better represent the degradation state of the bearings. The original data and generated data are mixed to realize data augmentation. At the same time, the dynamic time warping (DTW) algorithm is introduced to measure the similarity of the dataset, establishing the mapping relationship between the training set and target sequence, thereby enhancing the prediction accuracy of supervised learning. Experiments employing the XJTU-SY rolling element bearing accelerated life test dataset, IMS dataset, and pantograph data indicate that the proposed method yields high accuracy in bearing RUL prediction.

Keywords

• data augmentation
• remaining useful life (RUL) prediction
• dynamic time warping (DTW)