Remaining useful life prediction of rolling bearing based on anomaly correction

The remaining useful life (RUL) prediction based on deep learning obtains certain prediction results by training model with deterministic parameters. However, this method ignores the error range of parameter estimation, resulting in the inability to fully characterise the uncertainty. Therefore, a RUL prediction method based on anomaly correction is proposed. Firstly, to address the problem of RMS deviating from the degradation trajectory caused by external random factors, this paper proposes an adaptive time window anomaly correction strategy with sliding windows are used to construct degenerate slope. Secondly, to solve the problem of prediction result errors caused by changing random factors, this paper considers heteroscedasticity in prognosis and constructs a probability density function library to dynamically match the probability distribution. Then, aiming at the mean value as the prediction result may lead to overconfidence, a probability-based weighted enhanced method is proposed to improve the prediction accuracy. Finally, two datasets are used to verify the effectiveness and superiority of the proposed method.