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 event are stochastic in nature with irregular sampling frequency, which is challenging for current methods. For most prognostic applications, the RUL is closely correlated with changes in data trend exhibited in the observer signals. Motivated by this phenomenon, this paper proposes a novel Time Series-Histogram of Features method, which extracts features describing the local degradation features exhibited by observer signals in a moving time window. The proposed method is illustrated via a case study on a benchmark simulated aero-engine dataset. Results indicate that the proposed methodology performs as well as or better than conventional feature extraction methods on the same time window of information. Furthermore, it is also shown that the proposed method extracts information complementary to conventional feature extraction techniques, thus resulting in superior performance by combining these feature extraction techniques.