Similarity-based remaining useful life prediction method under varying operational conditions

Remaining useful life (RUL) prediction is the core task of prognostic and health management (PHM). A similarity-based RUL prediction method under varying operational conditions was presented. Similarity-based RUL prediction method does not need to build a model for entire complex system but can provide reasonable results, which is promising in engineering practice. However, the operational conditions such as different working loads and environmental conditions are not considered for degradation modeling. Therefore, this method combines basic similarity-based method and the effect of operational conditions to achieve better RUL prediction accuracy. Degradation models with different operational conditions were built by training units, and the RUL prediction was achieved by matching corresponding model using the real-time operational conditions of the running unit. The proposed degradation models describe the degradation process more precisely by taking the differences of operational conditions into account. According to the same accuracy standard, multi-group numerical experiments were finished by basic similarity-based method and the proposed method. The result shows the proposed method has a higher accuracy in RUL prediction.