Similarity-based residual life prediction method based on dynamic time scale and local similarity search

Residual useful life (RUL) prediction is the core of prognostics and health management. Similarity-based residual life prediction (SbRLP) is vital in RUL prediction due to its independence from degradation modeling, as well as high accuracy and robustness in prediction. However, researchers typically adopt a fixed time scale and global similarity search to perform similarity measurement, leading to considerable prediction errors and prolonged prediction times. Hence, a novel SbRLP method based on a dynamic time scale and local similarity search is proposed herein. First, the monitoring variables are reduced using the variable selection method based on multilayer information overlap. Next, the health states of reference samples are divided into five states using the K-means algorithm and the health states of the operating sample are recognized using the L-KNN algorithm. Further, dynamic time scales of the operating and reference samples are determined based on their length proportions of degradation trajectory at different prediction times. The local similarity search intervals of reference samples are obtained based on their health state levels. Next, the RULs of the operating sample are predicted using the local similarity search intervals and dynamic time scales. Finally, the effectiveness and superiority of the enhanced SbRLP are demonstrated using the commercial modular aero-propulsion system simulation dataset. The results reveal that the enhanced SbRLP yields a more accurate and efficient prediction of RUL in comparison with alternative methods.