Degradation trend feature generation improved rotating machines RUL prognosis method with limited run-to-failure data

Abstract

Abstract The success of rotating machines’ data-driven remaining useful life (RUL) prognosis approaches depends heavily on the abundance of entire life cycle data. However, it is not easy to obtain sufficient run-to-failure data in industrial practice. Data generation technology is a promising solution for enriching data but fails to address the intrinsic complexity of nonlinear stage degradation and the time correlation of long-term data. This research proposes an RUL prognosis approach improved by the degradation trend feature generation variational autoencoder. First, this study develops a framework combining degradation trend generation features to resolve the issue of capturing the elements of time distribution for run-to-failure data. Second, a generation variational autoencoder network with a tendency block is proposed to create high-quality time series data correlation features. Third, original and created degradation trend features are subjected to deep adaptive fusion and health indicator extraction. A bi-directional long short-term memory network is employed to predict the degradation trend and obtain the RUL prognosis. Finally, the proposed approach’s feasibility is confirmed by cross-validation experiments on a bearing dataset, which reduces the prediction error by 22.309%.