Bi-LSTM-Based Two-Stream Network for Machine Remaining Useful Life Prediction

Abstract

In industry, prognostics and 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, long short-term memory (LSTM) and convolutional neural networks (CNNs) are adopted into many RUL prediction approaches, which shows 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 first 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. In addition, to effectively integrate our proposed HFFs with the raw input signals, a novel bidirectional LSTM (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 commercial modular aero propulsion system simulation (C-MAPSS) dataset, showing superior performances over state-of-the-art approaches.