RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting

Abstract

Based on the features extracted from the condition monitoring data, data-driven prognostic approaches are able to predict the remaining useful life (RUL) of machinery. Existing methods usually assume that a certain feature contributes consistently to the prediction results during the operation. In fact, the degradation sensitivity of each feature varies with time in most practical cases, which limits the prediction accuracy of RUL. To tackle this issue, a novel convolutional-vector fusion network (C-VFN) is proposed in this paper. A vector-dynamic weighted fusion (V-DWF) algorithm is designed to dynamically evaluate the degradation sensitivity of each feature over time. The fluctuations of feature sensitivities over time are visualized through a weight map. Then, the sensitivity weights are assigned to the corresponding features to estimate the RUL. Meanwhile, the insensitive features are iteratively eliminated through a mechanism of RUL-result-oriented feedback. The proposed model is validated using accelerated degradation data of axle reducers and XJTU-SY datasets. The experimental results show that the C-VFN is able to estimate the degradation sensitivity of each feature along with time and improve the accuracy of RUL prediction.