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

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 timevarying 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 Levy 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.

Automated summary

This paper presents a hybrid Direct-Indirect Fusion (DIF) method that combines direct and indirect degradation characteristics (DCs) to predict the remaining useful life (RUL) of rotating machinery under time-varying conditions. The proposed framework can account for time-varying covariates, convert indirect DCs to direct DCs with a loop-generative adversarial network (Loop-GAN), and describe gradual degradation and sudden shocks in direct DCs with Levy processes. The results from simulation examples and an industrial application demonstrate that the proposed framework outperforms several benchmarks in predicting the degradation path and RUL of rotating machinery.