Framework and development of fault detection classification using IoT device and cloud environment

Journal of Manufacturing Systems

Volumn 43, Issue , 2017, Pages 257-270

  Lee, Hyunsoo ^a  

^a Kumoh National Institute of Technology   (South Korea)

Author keywords

Cloud computing; Cyber physical system (CPS); Deep belief network based deep learning (DBN DL); Fault detect classification (FDC); Internet of Things (IoT)

Indexed keywords

AUTOMOBILE MANUFACTURE; CABLES; CLOUD COMPUTING; CYBER PHYSICAL SYSTEM; DEEP LEARNING; DISTRIBUTED COMPUTER SYSTEMS; ELECTRIC CONTROL EQUIPMENT; ELECTRIC NETWORK TOPOLOGY; EMBEDDED SYSTEMS; FAULT DETECTION; INDUSTRIAL RESEARCH; MANUFACTURE; NETWORK ARCHITECTURE; PROCESS CONTROL; QUALITY CONTROL;

CYBER-PHYSICAL SYSTEMS (CPS); DEEP BELIEF NETWORKS; FAULT DETECT; HIGH INTENSITY DISCHARGES; INTERNET OF THINGS (IOT); MANUFACTURING PROCESS; PRODUCTION ENVIRONMENTS; QUEUEING NETWORK MODEL;

INTERNET OF THINGS;

EID: 85014081409     PISSN: 02786125     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmsy.2017.02.007     Document Type: Article

References (32)

1. [1] Monostori, L., Kardar, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., et al. Cyber-physical systems in manufacturing. CIRP Ann Manuf Technol 65:2 (2016), 621–641.
2. [2] Adamson, G., Wang, L., Holm, M., Moore, P., Cloud manufacturing—a critical review of recent development and future trends. Int J Comput Integr Manuf, 2015, 1–34.
3. [3] Herterich, M.M., Uebernickel, F., Brenner, W., The impact of cyber-physical systems on industrial services in manufacturing. Proc CIRP 30 (2015), 323–328.
4. [4] Zhong, R.Y., Dai, Q., Qu, T., Hu, G., Huang, G.Q., RFID-enabled real-time manufacturing execution system for mass-customization production. Robot Comput Integr Manuf 29:2 (2016), 283–292.
5. [5] Khaitan, S.K., McCalley, J.D., Design techniques and applications of cyber physical systems: a survey. IEEE Syst J 9:2 (2015), 350–365.
6. [6] Wu, D., Rosen, D.W., Schaefer, D., Scalability planning for cloud-based manufacturing systems. Journal of Manufacturing Science and Engineering 137:4 (2015), 1–13.
7. [7] National Science Foundation (NSF), Cyber Physical System NSF10515, http://www.nsf.gov/pubs/2010/nsf10515/nsf10515.htm, 2013.
8. [8] National Science Foundation (NSF), EAGER/Cyber Manufacturing Systems: Fleet-sourced Cyber Manufacturing Applications for improved Transparency and Resilience of Manufacturing Assets and Systems, http://www.nsf.gov/awardsearch/showAward?AWD_ID=1550433&HistoricalAwards=false, 2015.
9. [9] Evans, D., The Internet of Things: How the Next Evolution of the Internet is Changing Everything. 2011, CISCO, White Paper, 1–11.
10. [10] Lee, H., Hong, H., Dynamic sensor fusion and control framework of ioT-based device using classification restricted Boltzmann machine. Int J Appl Eng Res 10:90 (2015), 482–487.
11. [11] Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, Kewen, A review of process fault detection and diagnosis part III: process history based methods. Comput Chem Eng 27 (2003), 327–346.
12. [12] Qu, T., Thurer, M., Wang, J., Wang, Z., Fu, H., Li, C., et al. System dynamics analysis for an Internet-of-Things-enabled production logistics system. Int J Prod Res 54 (2016), 1–28.
13. [13] Zhang, L., Luo, Y., Tao, F., Li, B.H., Ren, L., Zhang, X., et al. Cloud manufacturing: a new manufacturing paradigm. Enterprise Inf Syst 8:2 (2014), 167–187.
14. [14] Wu, D., Jennings, C., Terpenny, J., Kumara, S., Cloud-based machine learning for predictive analytics: prediction of tool wear. IEEE International Conference on Big Data, Washington D.C., 2016, 1–6.
15. [15] Wu, D., Terpenny, J., Schaefer, D., Digital Design and Manufacturing on the Cloud: A Review of Software and Services, Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 2016, Cambridge University Press.
16. [16] Bishop, C.M., Pattern Recognition and Machine Learning. 2009, Springer, Berkeley, CA.
17. [17] Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2001, Springer, Canada.
18. [18] Lee, H., Banerjee, A., Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization. Proceedings of the 2015 Winter Simulation Conference, LA, 2015, 1163–1171.
19. [19] Zhong, R.Y., Xu, C., Chen, C., Huang, G.Q., Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 52 (2015), 1–12.
20. [20] Intel© Internet of Things (IoT) Developer Kit: IoT Cloud-based Analytics User Guide, pp. 1–21, 2014.
21. [21] Intel© Intel Edison Development Platform, pp. 1–3, 2014.
22. [22] Intel © Intel Edison Module Hardware Guide, pp. 1–28, 2014.
23. [23] Watthanawisuth, N., Maturos, T., Sappat, A., Tuantranont, A., The IoT wearable stretch sensor using 3D-graphene foam. IEEE Sens, 2015, 2015.
24. [24] Perera, C., Liu Jayawardena, C.H.S., Chen, M., A survey on internet of things from industrial market perspective. IEEE Access 2 (2014), 1660–1679.
25. [25] Rajkumar, R., Lee, I., Sha, L., Stankovic, J., Cyber-physical systems: the next computing revolution. Proceedings of 47th IEEE/ACM Design Automation Conference, 2010, 731–736.
26. [26] Gunes, V., Peter, S., Givargis, T., Vahid, F., A survey on concepts, applications, and challenges in cyber-physical system. KSII Trans Internet Inf Syst 8:12 (2014), 4242–4268.
27. [27] Cerekci, A., Banerjee, A., Dynamic control of the batch processor in a serial-batch processor system with mean tardiness performance. Int J Prod Res 48:5 (2010), 1339–1359.
28. [28] Wu, D., Rosen, D.W., Schaefer, D., Modeling and analyzing the material flow of crowdsourcing processes in cloud based manufacturing systems using stochastic petri nets. Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference, Ann Arbor, Michigan, 2014, 1–9.
29. [29] Curry, G.L., Feldman, R.M., Manufacturing Systems Modeling and Analysis. 2nd ed., 2011, Springer, New York, 1–338.
30. [30] Buzacott, J.A., Shanthikumar, J.G., Stochastic Models of Manufacturing Systems. 1963, Prentice Hall, Englewood Cliffs, NJ.
31. [31] Hopp, W.J., Spearman, M.L., Factory Physics. 2011, Waveland Pr. Inc, 1–720.
32. [32] Hinton, G.E., Osindero, S., Ther, Y., A fast learning algorithm for deep belief nets. J Neural Computn 18:7 (2006), 1527–1554.