Novel failure prognostics approach with dynamic thresholds for machine degradation

Estimating remaining useful life (RUL) of critical machinery is a challenging task. It is achieved through essential steps of data acquisition, data pre-processing and prognostics modeling. To estimate RUL of a degrading machinery, prognostics modeling phase requires precise knowledge about failure threshold (FT) (or failure definition). Practically, degrading machinery can have different levels (states) of degradation before failure, and prognostics can be quite complicated or even impossible when there is absence of prior knowledge about actual states of degrading machinery or FT. In this paper a novel approach is proposed to improve failure prognostics. In brief, the proposed prognostics model integrates two new algorithms, namely, a Summation Wavelet Extreme Learning Machine (SWELM) and Subtractive-Maximum Entropy Fuzzy Clustering (S-MEFC) to predict degrading behavior, automatically identify the states of degrading machinery, and to dynamically assign FT. Indeed, for practical reasons there is no interest in assuming FT for RUL estimation. The effectiveness of the approach is judged by applying it to real dataset in order to estimate future breakdown of a real machinery.