Multiple Sensors Based Prognostics With Prediction Interval Optimization via Echo State Gaussian Process

Abstract

In prognostics and health management, multiple sensors have been widely used to monitor the health condition of complex machines. As each sensor provides partial and dependent information, data-level fusion techniques have been developed and aim at increasing the reliability and safety of machines by providing the current health assessment and remaining useful life (RUL) prediction. While most existing data-level fusion techniques have shown a promise for prognostics, they are mainly limited by only focusing on the improvement of point prediction, which makes it difficult to provide adequate available information for decision-making in predictive maintenance. In this paper, the prediction interval (PI) is adopted to model the prediction uncertainty of RUL for its nature of high variability. An improved echo state Gaussian process (IESGP), which is able to provide an estimation of prediction uncertainty, is developed as a novel data-driven approach for RUL prediction and PI construction. The proposed IESGP is a novel Bayesian approach which combines the merits of echo state networks and Gaussian processes to enhance the prediction accuracy. Based on this, a comprehensive cost function is constructed to characterize the accuracy and uncertainty simultaneously, then a multi-objective genetic algorithm (MOGA) is applied to optimize the point prediction and PI. The validity of the proposed approach is verified on the widely used turbofan benchmark datasets. The experimental results show that the proposed approach can not only achieve superior predictive accuracy in comparison with several state-of-the-art approaches but also obtain PIs with high quality.

Keywords

• Bayesian inference
• echo state networks
• multi-objective optimization
• prediction uncertainty
• predictive intervals
• remaining useful life