A Multi-Sensor Prognostics Framework Based on Data Fusion and Time Series Similarity Search

Predicting the remaining useful life (RUL) of systems is of importance in prognostics and health management to improve the maintenance capability in modern industries. Several approaches for RUL prediction using multiple sensory data assume the system's degradation in a specific form, which may not be applicable to the diversity of practical engineering systems. In this paper, a two-phase data driven framework is proposed. The first phase is an offline process of constructing the health index (HI) library from run-to-failure data using fuzzy similarity based data fusion, which enables to create the HIs without any assumptions. In the second phase, the HI of online is mapped onto the library created offline to find the best matching position using time series similarity search. This simultaneously enables the current condition of the system being informed as well as estimating its time left. The degradation simulation dataset of turbofan engine from NASA Prognostics Center is used to evaluate the potential application of the proposed framework.