Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning

Abstract

Due to the recent development of cyber-physical systems, big data, cloud computing, and industrial wireless networks, a new era of industrial big data is introduced. Deep learning, which brought a revolutionary change in computer vision, natural language processing, and a variety of other applications, has significant potential for solutions providing in sophisticated industrial applications. In this paper, a concept of device electrocardiogram (DECG) is presented, and an algorithm based on deep denoising autoencoder (DDA) and regression operation is proposed for the prediction of the remaining useful life of industrial equipment. First, the concept of electrocardiogram is explained. Then, a problem statement based on manufacturing scenario is presented. Subsequently, the architecture of the proposed algorithm called integrated DDA and the algorithm workflow are provided. Moreover, DECG is compared with traditional factory information system, and the feasibility and effectiveness of the proposed algorithm are validated experimentally. The proposed concept and algorithm combine typical industrial scenario and advance artificial intelligence, which has great potential to accelerate the implementation of industry 4.0.

Keywords

• Cyber-physical systems
• deep learning
• device electrocardiogram
• industrial big data
• industry 40