RUL Prediction and Uncertainty Management for Multisensor System Using an Integrated Data-Level Fusion and UPF Approach
Heng Zhang, Enhui Liu, Bin Zhang, Qiang Miao
Abstract
Due to the fact that single sensor data contains only partial information about complex systems, multiple sensors are often embedded to simultaneously monitor the health state and predict the remaining useful life (RUL). This brings new challenges to traditional approaches that focus on single sensor in terms of data fusion, optimization, and uncertainty management. To address these challenges, this article proposes a novel RUL prediction and uncertainty management framework for multisensor systems. In this framework, a composite 1-D health indicator (1-D HI) is obtained from multiple sensors by optimizing some HI characteristics, including monotonicity, robustness, fitting error, and range information, to better describe the underlying degradation process. A multiobjective grasshopper optimization algorithm is used to achieve the optimal weight vector of the fusion model. Then, an unscented particle filter is introduced to predict the RUL by combining the degradation model constructed from HI and the composite 1-D HI as measurement. To manage the uncertainty in prognosis, a probability distribution of failure threshold and noise parameter adjustment are developed. Experimental results on aircraft turbine engine degradation and comparison with state-of-the-art methods are presented to demonstrate the effectiveness of the proposed framework in RUL prediction of multisensor systems.