Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning.

RUL (remaining useful life) shapelets were recently developed to overcome the shortcomings of similarity-based RUL prediction methods, such as high sensitivity to parameters. RUL shapelets are informative subsequences whose distances to a run-to-failure time series sample are very useful for predicting the RUL of the sample. However, the prediction performance and interpretability highly depend on the set of RUL shapelets, and it is very difficult to compose an optimized set.