A hybrid prognostic method for rotating machinery under time-varying operating conditions by fusing direct and indirect degradation characteristics

Abstract

Condition monitoring technologies can provide sensor data for predicting the remaining useful life (RUL) of rotating machinery. However, it is often difficult to obtain direct degradation characteristics (DCs), which directly reflect the health of a machine, in real-time. Instead, indirect DCs, which are collected under time-varying operating conditions, are often used. Traditional machine learning and model-based prognostic methods may not be effective when handling such data. This paper presents a hybrid Direct-Indirect Fusion (DIF) method that combines direct and indirect DCs to predict the RUL of rotating machinery under time-varying conditions. The framework can account for time-varying covariates, convert indirect DCs to direct DCs under moderate sample sizes with a loop-generative adversarial network (Loop-GAN), and describe gradual degradation and sudden shocks in direct DCs with Lévy processes. The proposed framework outperforms several benchmarks in predicting the degradation path and RUL of rotating machinery as demonstrated in both simulation examples and an industrial application.