Full Text:   <2085>

Summary:  <1493>

CLC number: TP391

On-line Access: 2019-12-10

Received: 2019-04-14

Revision Accepted: 2019-08-12

Crosschecked: 2019-10-14

Cited: 0

Clicked: 5522

Citations:  Bibtex RefMan EndNote GB/T7714


Bao-rui Li


Ke-sheng Wang


Ke-sheng Wang


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.11 P.1493-1504


Framework and case study of cognitive maintenance in Industry 4.0

Author(s):  Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang

Affiliation(s):  Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China; more

Corresponding email(s):   kesheng.wang@ntnu.no

Key Words:  Cognitive maintenance, Industry 4.0, Cutting-edge equipment, Deep learning, Green monitor, Smart manufacturing factory

Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang. Framework and case study of cognitive maintenance in Industry 4.0[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1493-1504.

@article{title="Framework and case study of cognitive maintenance in Industry 4.0",
author="Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Framework and case study of cognitive maintenance in Industry 4.0
%A Bao-rui Li
%A Yi Wang
%A Guo-hong Dai
%A Ke-sheng Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 11
%P 1493-1504
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900193

T1 - Framework and case study of cognitive maintenance in Industry 4.0
A1 - Bao-rui Li
A1 - Yi Wang
A1 - Guo-hong Dai
A1 - Ke-sheng Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 11
SP - 1493
EP - 1504
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900193

We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making techniques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting- edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Bengio Y, 2009. Learning deep architectures for AI. Found Trends Mach Learn, 2(1):1-127.

[2]Chen JF, Jin QJ, Chao J, 2012. Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin. Math Probl Eng, 2012:235929.

[3]Cheng J, Wang PS, Li G, et al., 2018. Recent advances in efficient computation of deep convolutional neural networks. Front Inform Technol Electron Eng, 19(1):64-77.

[4]de Bruin T, Verbert K, Babuška R, 2017. Railway track circuit fault diagnosis using recurrent neural networks. IEEE Trans Neur Netw Learn Syst, 28(3):523-533.

[5]Deng L, 2014. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inform Process, 3, Article e2.

[6]Din GMU, Marnerides AK, 2017. Short term power load forecasting using deep neural networks. Int Conf on Computing, Networking and Communications, p.594-598.

[7]Galloway GS, Catterson VM, Fay T, et al., 2016. Diagnosis of tidal turbine vibration data through deep neural networks. Proc 3rd European Conf of the Prognostics and Health Management Society, p.172-180.

[8]Gan M, Wang C, Zhu CA, 2016. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Process, 72-73:92-104.

[9]Graves A, Mohamed AR, Hinton G, 2013. Speech recognition with deep recurrent neural networks. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.6645-6649.

[10]Hanson J, Yang Y, Paliwal K, et al., 2016. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks. Bioinformatics, 33(5): 685-692.

[11]Hinton GE, 2002. Training products of experts by minimizing contrastive divergence. Neur Comput, 14(8):1771-1800.

[12]Hinton GE, Salakhutdinov RR, 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786): 504-507.

[13]Huang WH, Song GJ, Hong HK, et al., 2014. Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst, 15(5): 2191-2201.

[14]Jia F, Lei YG, Lin J, et al., 2016. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process, 72-73:303-315.

[15]Kuremoto T, Kimura S, Kobayashi K, et al., 2014. Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137:47-56.

[16]Lee J, Bagheri B, Kao HA, 2015. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett, 3:18-23.

[17]Li L, Dai GL, Zhang Y, 2017. A membership-based multi- dimension hierarchical deep neural network approach for fault diagnosis. 29th Int Conf on Software Engineering and Knowledge Engineering, p.197-200.

[18]Li Z, Wang Y, Wang K, 2017a. A data-driven method based on deep belief networks for backlash error prediction in machining centers. J Intell Manuf, p.1-13.

[19]Li Z, Wang Y, Wang K, 2017b. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Adv Manuf, 5(4):377-387.

[20]Ma Q, Tanigawa I, Murata M, 2014. Retrieval term prediction using deep belief networks. J Nat Lang Process, 22(4): 225-250.

[21]Malhotra P, Tv V, Ramakrishnan A, et al., 2016. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. https://arxiv.org/abs/1608.06154

[22]Palangi H, Deng L, Shen YL, et al., 2016. Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE/ ACM Trans Audio Speech Lang Process, 24(4):694-707.

[23]Ribeiro B, Lopes N, 2011. Deep belief networks for financial prediction. Int Conf on Neural Information Processing, p.766-773.

[24]Sak H, Senior AW, 2017. Processing Acoustic Sequences Using Long Short-Term Memory (LSTM) Neural Networks That Include Recurrent Projection Layers. US Patent 962 010 8. http://www.freepatentsonline.com/9620108.html

[25]Schmidhuber J, 2015. Deep learning in neural networks: an overview. Neur Netw, 61:85-117.

[26]Shin HC, Orton MR, Collins DJ, et al., 2013. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans Patt Anal Mach Intell, 35(8):1930-1943.

[27]Tamilselvan P, Wang PF, 2013. Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf, 115:124-135.

[28]Wang KS, 2014. Key technologies in intelligent predictive maintenance (IPdM)―a framework of intelligent faults diagnosis and prognosis system (IFDaPS). Adv Mater Res, 1039:490-505.

[29]Wang KS, Li Z, Braaten J, et al., 2015. Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs. Adv Manuf, 3(2):97-104.

[30]Wang L, Zhang ZJ, Long H, et al., 2017. Wind turbine gearbox failure identification with deep neural networks. IEEE Trans Ind Inform, 13(3):1360-1368.

[31]Wang Y, Ma HS, Yang JH, et al., 2017. Industry 4.0: a way from mass customization to mass personalization production. Adv Manuf, 5(4):311-320.

[32]Yu B, Kumbier K, 2018. Artificial intelligence and statistics. Front Inform Technol Electron Eng, 19(1):6-9.

[33]Zabalza J, Ren JC, Zheng JB, et al., 2016. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing, 185:1-10.

[34]Zhang ZY, Wang KS, 2014. Wind turbine fault detection based on SCADA data analysis using ANN. Adv Manuf, 2(1):70-78.

[35]Zhao R, Wang JJ, Yan RQ, et al., 2016. Machine health monitoring with LSTM networks. 10th Int Conf on Sensing Technology, p.1-6.

[36]Zhao R, Yan RQ, Chen ZH, et al., 2019. Deep learning and its applications to machine health monitoring. Mech Syst Signal Process, 115(15):213-237.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE