Remaining Useful Life Prediction using Deep Learning Approaches: A Review
Youdao Wang, Yifan Zhao, Sri Addepalli
Abstract
Data-driven techniques, especially on artificial intelligence (AI) such as deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the growth of industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of the industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper gives a brief introduction of RUL prediction and reviews the start-of-the-art DL approaches in terms of four main representative deep architectures, including Auto-encoder, Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). It has been observed that DL techniques attract growing interests on RUL prediction that suggests a promising future of their applications in manufacturing.