Automatic and Generic Prognosis Method Based on Data Trend Analysis and Neural Network

Abstract

This paper presents a generic and unsupervised failure prognosis method which can be applied to wide scope of applications. The main contribution of the presented method is automatic relevant data identification based on signal smoothing and trendability analysis and automatic degradation model identification for health indices construction, built using a trained neural network, thus allowing for the automatic adaptation of the degradation trend model to changes in the degradation dynamic. Regarding the failure prognosis, the end of life is first predicted using a fitting model; then, the remaining useful life is predicted using a similarity algorithm. The proposed approach is validated using the turbofan engine data sets provided by NASA. The prediction results have been evaluated using accuracy metrics such as root mean square error and prognostic metrics such α−λ and relative accuracy. The obtained results show the effectiveness of the proposed method, both for the end of life and remaining useful life predictions.