Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning
Open Access
- 27 February 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 6, 17190-17197
- https://doi.org/10.1109/access.2018.2809681
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
Funding Information
- Natural Science Foundation of Guangdong Province, China (2015A030313746, 2017B030311008)
- National Key Research and Development Project (2017YFE0101000)
- Major Projects for Numerical Control Machine (2015ZX04005001)
- Natural Science Foundation of Hubei province, China (2014CFB637)
- Research Fund Program of Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing (CIMSOF2016004)
This publication has 32 references indexed in Scilit:
- Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordinationComputer Networks, 2016
- Software-Defined Industrial Internet of Things in the Context of Industry 4.0IEEE Sensors Journal, 2016
- An Unlicensed Taxi Identification Model Based on Big Data AnalysisIEEE Transactions on Intelligent Transportation Systems, 2015
- Deep learningNature, 2015
- CaffePublished by Association for Computing Machinery (ACM) ,2014
- VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud ComputingMobile Networks and Applications, 2014
- Towards Key Issues of Disaster Aid based on Wireless Body Area NetworksKSII Transactions on Internet and Information Systems, 2013
- A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assetsExpert Systems with Applications, 2011
- Residual Life Predictions From Vibration-Based Degradation Signals: A Neural Network ApproachIEEE Transactions on Industrial Electronics, 2004
- Practical selection of SVM parameters and noise estimation for SVM regressionNeural Networks, 2004