Autoencoder Quasi-Recurrent Neural Networks for Remaining Useful Life Prediction of Engineering Systems

• Published in: IEEE/ASME transactions on mechatronics Vol. 27; no. 2; pp. 1081 - 1092
• Main Authors: Cheng, Yiwei, Hu, Kui, Wu, Jun, Zhu, Haiping, Shao, Xinyu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Autoencoder; Convolution; Data processing; Datasets; deep learning (DL); Engines; Feature extraction; Life prediction; Logic gates; Machining; Maintainability; Monitoring; multisensor data; Neural networks; Predictive models; quasi-recurrent neural networks (QRNN); Recurrent neural networks; Reliability engineering; remaining useful life (RUL); Roller bearings; Signal monitoring; Training; Turbofan engines; Useful life
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2021.3079729

Remaining useful life (RUL) prediction is a key solution to improve the reliability, availability, and maintainability of engineering systems. Long short-term memory (LSTM) and convolution neural networks (CNN) are the current hotspots in the field of RUL prediction. However, the LSTM-based prognostic approach has a slow loop step to process large-scale time-series data since the dependence of the data processing process at each time on the output of the previous time limits parallelism, and the CNN-based prognostic approach is not fit for time-series data although it can process the data in parallel. In this article, a new autoencoder quasi-recurrent neural networks (AEQRNN) based prognostic approach is proposed for RUL prediction of the engineering systems. The AEQRNN contains convolution components that can process input data in parallel, and pooling components which has two LSTM-like gate structures to process time-series data. In addition, the AEQRNN can automatically extract hidden features from monitoring signals without manual feature design. The effectiveness of the proposed prognostic approach is validated by three prognostic benchmarking datasets, including a turbofan engine dataset, a rolling bearing dataset, and a machining tool dataset. Experimental results demonstrate that this approach has both superior prognostic performance and training speed in comparison with other kinds of recurrent-neural-network-based approaches and various state-of-the-art approaches in the recent literature.