A Health Index Construction Framework for Prognostics Based on Feature Fusion and Constrained Optimization

Abstract

The actual health state of a system is difficult to measure in online applications. Hence, features extracted from the sensor data are usually fused as a composite health index (HI) to represent the system status. If the HI shows explicit increasing or decreasing trend, prognostic methods are applied to extrapolate the future degradation and predict the remaining useful life (RUL). However, the practical requirements of prognostics for HI construction are often not considered. Thus, the advanced index may not capture the fault progression well, which would lead to inaccurate predictions. In this article, an HI construction frame-work based on feature fusion and constrained optimization is proposed. Multiple features are fused with weighted sum and nonlinear fusion functions. Three desired properties of HI for prognostics are considered to build an optimization model of feature weights. To solve this multi-variable model effectively, a self-adaptive differential evolution (SADE) algorithm is proposed. Then, a state-space model based on Wiener process is applied to the constructed HIs for online RUL prediction. To evaluate the performance of the HIs, a consistency-based metric is developed. Illustrative examples using two industrial datasets demonstrate the efficiency and adaptability of the proposed methods.