A novel multi-sensor data fusion enabled health indicator construction and remaining useful life prediction of aero-engine

Remaining useful life (RUL) prediction is vital to formulate a suitable maintenance strategy in manufacturing systems health management. Multisensor data fusion of complex engineering systems has attracted substantial attention due to the fact that a single sensor can only collect partial information. Health indicator (HI) construction plays a crucial role in multisensor data fusion and machinery prognostic, mainly because it attempts to quantify a history and ongoing degradation process by fusing the advantages of multiple sensors. However, large numbers of coefficients are involved for most of the existing HIs. Additionally, simplifications during modeling may inhibit the wide application of the constructed HI. To address these two challenges, a new multisensor data fusion method is proposed in this paper by constructing a HI for the characterization of the degradation process. Firstly, the sensors that collect invalid data or conflicting data are removed through a correlation coefficient operation. Then, principal component analysis (PCA) is adopted to reduce the number of coefficient before constructing the HI. Furthermore, the objective function is constructed under the comprehensive consideration of the three factors of the HI, that is, monotonicity, trendability, and fitting errors. The effectiveness of the proposed method is verified using the C-MAPSS dataset. Multiple comparison results show that the HI possesses excellent performance in both degradation characterization and remaining useful life prediction.