A Novel Time Series-Histogram of Features (TS-HoF) Method for Prognostic Applications.

Data-driven prognostic methods typically make use of observer signals reflective of the system health combined with machine learning methods to predict the Remaining Useful Life (RUL) of the system. Currently, majority of feature extraction methods developed for prognostics focused on extracting features from regular time series applications. However, events-data collected during occurrence of an ...