Heterogeneous bi-directional recurrent neural network combining fusion health indicator for predictive analytics of rotating machinery

Abstract

Data-driven intelligent methods arise the increasing demand for predictive analytics to evaluate the operational reliability and natural degradation of rotating machinery. Nevertheless, accurate and timely predictive analytics is still regarded as an extremely challenging mission, because the quality of predictive maintenance depends not only on the capability of intelligent model, but also on the construction of effective health indicators To overcome this issue, a novel heterogeneous bi-directional gated recurrent unit (GRU) model combining with fusion health indicator (Fusion-HI) is proposed for predictive analytics in this paper. First, the support evidence space is constructed to reflect the operating state of mechanical equipment. Then the evidence features from multiple domains are integrated to obtain the optimal Fusion-HI by the modified de-noising auto-encoder (MDAE). Finally, a hybrid prediction network is designed combining with the gate attention algorithm, which consists of multi-scale convolution layers, bi-directional GRU layers, smoothed and de-noised layers, and regression layers. Three experimental whole lifetime data and one industrial entire life cycle data are analyzed to validate the feasibility of the proposed approach in two case studies respectively. Relevant experimental results indicate that the Fusion-HI is capable to sensitively characterize the degradation state of equipment, while the prediction accuracy of presented heterogeneous model is superior to that of conventional prediction approaches.