Bearing performance degradation assessment and remaining useful life prediction based on data-driven and physical model

Abstract Intelligent health maintenance of bearings usually consists of two stages: constructing effective health assessment indicators and accurate remaining useful life prediction models. However, many prediction models are available only when many constraints are met, and the health assessment indicators may not be able to accurately track the performance degradation process of bearings. This study proposes a bearing performance degradation assessment and remaining life prediction method. First, the health evaluation index family (referred to as the generalized high-order moment coefficient) was constructed based on the generalized power mean and high-order origin moments for health assessment. Subsequently, an improved Paris–Erdogan model is proposed, which uses the optimal health evaluation index as input to predict the remaining life of the bearing after the initial failure. The experimental results show that the proposed method has a higher performance degradation tracking accuracy and a smaller prediction error than the combination of traditional statistical indicators and prediction models.