Jin, R., Chen, Z., Wu, K., Wu, M., Li, X., &amp; Yan, R. (2022). Bi-LSTM-Based Two-Stream Network for Machine Remaining Useful Life Prediction. IEEE Transactions on Instrumentation and Measurement, 71, 1–10. https://doi.org/10.1109/tim.2022.3167778
In industry, prognostic health management (PHM) is used to improve the system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failure and reducing operation cost. Recently, with the development of deep learning technology, the long short-term memory (LSTM) and convolutional neural networks (CNN) are adopted into many RUL prediction approaches, which show impressive performances. However, existing deep learning based methods directly utilize raw signals. Since noise widely exists in raw signals, the quality of these approaches' feature representation is degraded, which degenerates their RUL prediction accuracy. To address this issue, we firstly propose a series of new handcrafted feature flows (HFFs), which can suppress the raw signal noise, and thus improve the encoded sequential information for the RUL prediction. Additionally, to effectively integrate our proposed HFFs with the raw input signals, a novel Bi-LSTM based two-stream network is proposed. In this novel two-stream network, three different fusion methods are designed to investigate how to combine both streams' feature representations in a reasonable way. To verify our proposed Bi-LSTM based two-stream network, extensive experiments are carried out on the C-MAPSS dataset, showing superior performances over state-of-the-art approaches.
This research / project is supported by the A*STAR - Career Development Award
Grant Reference no. : C210112046