Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions

Remaining useful life (RUL) prediction is essential in enhancing the safety and reliability of rotating machinery. Deep learning techniques have been extensively researched and demonstrated promising results in RUL prediction tasks. But most existing models are designed for machinery equipment in a specific condition. In this case, a novel prediction method, knowledge-enhanced convolutional Transformer ensemble model (KE-CTEM), is proposed in this study. First, a feature extraction neural network (FENN) is introduced to extract features and transfer the working conditions information of existing datasets as knowledge to downstream RUL prediction tasks. Then, a convolutional Transformer model is leveraged to capture the input data degradation patterns and predict RUL values. Finally, knowledge-enhanced strategy and ensemble strategy are proposed to enhance the robustness of the model and improve the prediction accuracy. To verify the practicality and effectiveness of the proposed method, run-to-failure data of bearings from PRONOSTIA platform are utilized for RUL prognostics. Compared with several representative and state-of-the-art methods, the experimental results demonstrate the superiority and feasibility of the proposed method. And ablation study indicates the high efficiency and robustness of each module within the proposed model. Compared with representative RUL prediction methods, the proposed KE-CTEM demonstrates superior performance in terms of RMSE and MAPE with a reduction of 32.0% and 16.2%, respectively.