A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model. (February 2023) - Xu, Zifei; Bashir, Musa; Liu, Qinsong; Miao, Zifan; Wang, Xinyu; Wang, Jin; Ekere, Nduka
Highlights: A novel multiscale encoder-decoder is proposed for construction of health indicator. The health indicator's reliability is examined using four distance function. The proposed method offers more accurate means of find the HI's first prediction point. A fully intelligent bearing remaining lifetime prediction model was designed. Abstract: Construction of an intelligent Health Indicator (HI) that can accurately describe the degradation process is a prerequisite to accelerating the development of an automatic remaining useful life (RUL) prediction model for rotating machinery such as bearings. This research aims to develop an intelligent model that can predict the remaining useful life of bearings without physical human intervention. The intelligent HI model, named Multi-Scale-Multi-Head Attention with Automatic Encoder-Decoder (MSMHA-AED), is constructed based on an unsupervised neural network model and can extract multi-scale coded features of bearings from raw vibration signals. The model is fitted with an ensemble health indicator designed to fuse the metrics of healthy and damaged coded features of bearings to create a more reliable health indicator for RUL prediction. The intelligent HI model is subsequently used to develop three neural network-based prognostic models to examine the reliability of the proposed health indicator in RUL predictions. Using the prognostic model, the magnitude of degradation in the bearings is estimated by measuring the similarity between the coded features of healthy and unknown damaged bearings using three measurement methods. It was found that the similarity measured by the Wasserstein distance method offers more suitable results for damage quantification due to its unique capability of measuring health indicators in more monotonic condition. It is also found that the proposed model is less prone to giving false alarms even when used to detect degradation for the first time. All performance indicators of the proposed approach show better and more robust metrics than the state-of-art methods.