A novel evidence reasoning-based RUL prediction method integrating uncertainty information

The remaining useful life (RUL) prediction plays an essential role in maintenance decision making to ensure safe operation of mechanical equipment. However, it still remains challenges to mine and utilize the uncertainty information hidden behind the running data and to enhance the interpretability of the prediction process, which leads to inaccurate and unreasonable prediction results. Therefore, a novel evidence reasoning-based RUL prediction method integrating uncertainty information is proposed to overcome these limitations. First, a sample casting method is presented to transform the historical sample set of an input feature sequence into the corresponding referential life evidence matrix (RLEM), achieving almost lossless transformation from RUL about the input feature sequence to life evidence. Second, the real-time samples of multiple input features activate their corresponding RLEMs to obtain the activated life evidence, and use evidence reasoning (ER) rule to fuse these activated life evidence to ultimately obtain integrated life evidence. Then, to further improve the accuracy of integrated life evidence, the reliability of activated life evidence is calculated online through sample entropy method, and the weight of life evidence is optimized offline using genetic algorithm. Finally, the integrated life evidence is transformed into predicted values of RUL based on utility theory. Two case studies on rolling bearing and hydrogen fuel cell show the superiority of the proposed RUL prediction method.