Feature Fusion based Ensemble Method for remaining useful life prediction of machinery

Abstract

The signal analysis features and deep representation features have been widely utilized to predict the Remaining Useful Life (RUL) of machinery. However, existing studies rarely fuse these features for RUL prediction to explore their complementarity. Therefore, this paper proposes a Feature Fusion based Ensemble Method (FFEM) that makes full use of the characteristics of signal analysis features and deep representation features. First, features are extracted by signal analysis and deep learning methods, respectively. The time-domain features, frequency-domain features, and time–frequency​ domain features are extracted by different signal analysis methods, while the deep representation features are from the bidirectional long short-term memory networks. Then, an improved random subspace method is proposed, which fuses different types of features based on group-based sparse learning to use the complementarity among features. Furthermore, accurate and diverse base learners are generated and the aggregation strategy, mean rule, is adopted for predicting RUL. To validate the proposed FFEM, experiments on the run-to-failure datasets of bearings are conducted, and the experimental results verify that the proposed method greatly improves the RUL prediction performance and surpasses other existing ensemble learning methods.