Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach

Nowadays multiple sensors are widely used to simultaneously monitor the degradation status of a unit. Because those sensor signals are often correlated and measure different characteristics of the same unit, effective fusion of such a diverse “gene pool” is an important step to better understanding the degradation process and producing a more accurate prediction of the remaining useful life. To address this issue, this article proposes a novel data fusion method that constructs a composite Health Index (HI) via the combination of multiple sensor signals for better characterizing the degradation process. In particular, we formulate the problem as indirect supervised learning and leverage the quantile regression to derive the optimal fusion coefficient. In this way, the prognostic performance of the proposed method is guaranteed. To the best of our knowledge, this is the first article that provides the theoretical analysis of the data fusion method for degradation modeling and prognostics. Simulation studies are conducted to evaluate the proposed method in different scenarios. A case study on the degradation of aircraft engines is also performed, which shows the superior performance of our method over existing HI-based methods.