Predicting remaining useful life of turbofan engines using degradation signal based echo state network

Abstract

Abstract The field of Prognostics and Health Management (PHM) in industries is gaining greater popularity to achieve high reliability by shifting the preventive maintenance to predictive maintenance. Estimation of Remaining Useful Life (RUL) is an effective prognostic measure that forecasts the health state of machine based on degradation modelling and condition monitoring. This article proposes a novel and robust methodology that uses Reduced Affinity Propagation (RAP) clustering technique that extracts representatives from the temporal signals measured through various heterogeneous sensors to predict the RUL using Echo State Network (ESN) with dynamic lateral inhibiting connections. The main advantage of the proposed model is that it does not overlook the features from the degradation signals and also learns the natural mapping among the representative points from the integrated sensor value. This approach is verified using CMAPPS dataset to show hopeful results in predicting the RUL of aircraft turbo fan engine. Also, this methodology can be a deployed as a tool in real time industrial applications to schedule predictive maintenance activities.