Echo State Network and Particle Swarm Optimization for Prognostics of a Complex System

Abstract

To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely prognose faults. In this context, this paper describes a new intelligent approach to estimate the remaining useful life in complex systems. This approach is based on the combination of several intelligent techniques. This approach is based on Echo State Network (ESN) and Particle Swarm Optimization (PSO) technique to set the ESN with optimal parameters. The input of this model are the measurements of signals correlated to the component degradation state, whereas the model output is the component RUL. To validate the feasibility of the proposed approach, real life fault historical data from turbofan engines system were analyzed and used to obtain the optimal prediction of RUL.