A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

Journal of Manufacturing Systems

Volumn 43, Issue , 2017, Pages 25-34

  Wu, Dazhong ^a   Liu, Shaopeng ^b   Zhang, Li ^b   Terpenny, Janis ^a   Gao, Robert X ^c   Kurfess, Thomas ^d   Guzzo, Judith A ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b GE GLOBAL RESEARCH   (United States)

^c Case Western Reserve University   (United States)

^d GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Cyber Manufacturing; Fog computing; Industrial internet of things; Machine learning; Prognosis

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER CONTROL SYSTEMS; CUTTING TOOLS; ENERGY UTILIZATION; ENGINEERING EDUCATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MANUFACTURE; PREDICTIVE ANALYTICS; PROCESS MONITORING; REAL TIME SYSTEMS; WIRELESS SENSOR NETWORKS;

COMPUTATIONAL FRAMEWORK; DIAGNOSIS AND PROGNOSIS; HIGH PERFORMANCE COMPUTING; INDUSTRIAL INTERNETS; INTELLIGENT MANUFACTURING; ORIGINAL EQUIPMENT MANUFACTURERS; PROGNOSIS; SENSING TECHNOLOGY;

FOG COMPUTING;

EID: 85013912214     PISSN: 02786125     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmsy.2017.02.011     Document Type: Article

References (55)

1. [1] Wu, D., Greer, M.J., Rosen, D.W., Schaefer, D., Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32:4 (2013), 564–579.
2. [2] Wu, D., Rosen, D.W., Wang, L., Schaefer, D., Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput Aided Des 59 (2015), 1–14.
3. [3] Wu, D., Rosen, D.W., Schaefer, D., Scalability planning for cloud-Based manufacturing systems. J Manuf Sci Eng, 2015.
4. [4] Xu, X., From cloud computing to cloud manufacturing. Rob Comput Integr Manuf 28:1 (2012), 75–86.
5. [5] Mourtzis, D., Vlachou, E., Xanthopoulos, N., Givehchi, M., Wang, L., Cloud-based adaptive process planning considering availability and capabilities of machine tools. J Manuf Syst 39 (2016), 1–8.
6. [6] Zhang, Z., Li, X., Liu, Y., Xie, Y., Distributed Resource Environment: A Cloud-Based Design Knowledge Service Paradigm, Cloud-Based Design and Manufacturing (CBDM). 2014, Springer, 63–87.
7. [7] Lu, Y., Xu, X., Xu, J., Development of a hybrid manufacturing cloud. J Manuf Syst 33:4 (2014), 551–566.
8. [8] Wang, X.V., Xu, X.W., An interoperable solution for Cloud manufacturing. Rob Comput Integr Manuf 29:4 (2013), 232–247.
9. [9] Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., et al. Cloud-enabled prognosis for manufacturing. Cirp Ann Manuf Techn 64:2 (2015), 749–772.
10. [10] Yan, R., Gao, R.X., An efficient approach to machine health diagnosis based on harmonic wavelet packet transform. Rob Comput Integr Manuf 21:4 (2005), 291–301.
11. [11] Teti, R., Kumara, S., Intelligent computing methods for manufacturing systems. Cirp Ann Manuf Techn 46:2 (1997), 629–652.
12. [12] Zhang, L., Gao, R.X., Lee, K.B., Spindle health diagnosis based on analytic wavelet enveloping, Instrumentation and Measurement. Trans IEEE 55:5 (2006), 1850–1858.
13. [13] Wang, P., Gao, R.X., Adaptive resampling-based particle filtering for tool life prediction. J Manuf Syst 37 (2015), 528–534.
14. [14] Lee, J., Bagheri, B., Kao, H.-A., A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3 (2015), 18–23.
15. [15] Wang, P., Gao, R.X., Fan, Z., Cloud computing for cloud manufacturing: benefits and limitations. J Manuf Sci Eng, 137(4), 2015, 040901.
16. [16] Wang, L., Törngren, M., Onori, M., Current status and advancement of cyber-physical systems in manufacturing. J Manuf Syst 37:Part 2 (2015), 517–527.
17. [17] Lee, E.A., Cyber physical systems: design challenges, proc. object oriented real-Time distributed computing (ISORC). 2008 11th IEEE International Symposium on, IEEE, 2017, 363–369.
18. [18] NSF, Cyber-enabled materials, manufacturing and smart systems. 2015 https://www.nsf.gov/about/budget/fy2015/pdf/35_fy2015.pdf.
19. [19] NSF, Cybermanufacturing Systems. 2015 http://www.nsf.gov/pubs/2015/nsf15061/nsf15061.jsp.
20. [20] Horizon2020, Smart Cyber-Physical Systems. 2015 http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/ict-01-2016.html.
21. [21] Horizon2020, Digital automation. 2015 http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/fof-11-2016.html.
22. [22] Feitelson, D.G., The supercomputer industry in light of the Top500 data. Comput Sci Eng 7:1 (2005), 42–47.
23. [23] Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., et al. A view of cloud computing. Commun ACM 53:4 (2010), 50–58.
24. [24] Mell, P., Grance, T., The NIST definition of cloud computing. 2011.
25. [25] Zhang, Z., Liu, G., Jiang, Z., Chen, Y., A cloud-based framework for lean maintenance, repair, and overhaul of complex equipment. J Manuf Sci Eng, 137(4), 2015, 040908.
26. [26] Delen, D., Oztekin, A., Kong, Z.J., A machine learning-based approach to prognostic analysis of thoracic transplantations. Artif Intell Med 49:1 (2010), 33–42.
27. [27] Kambatla, K., Kollias, G., Kumar, V., Grama, A., Trends in big data analytics. J Parallel Distr Com 74:7 (2014), 2561–2573.
28. [28] Shin, S.-J., Woo, J., Rachuri, S., Predictive analytics model for power consumption in manufacturing. Procedia CIRP 15 (2014), 153–158.
29. [29] Wright, P., Dornfeld, D., Ota, N., Condition monitoring in end-milling using wireless sensor networks (WSNs). Trans NAMRI/SME, 2008, 36.
30. [30] Rangwala, S., Dornfeld, D., Sensor integration using neural networks for intelligent tool condition monitoring. J Eng Ind 112:3 (1990), 219–228.
31. [31] Li, C.J., Li, S., Acoustic emission analysis for bearing condition monitoring. Wear 185:1 (1995), 67–74.
32. [32] Lu, B., Gungor, V.C., Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks. Ieee T Ind Electron 56:11 (2009), 4651–4659.
33. [33] Hou, L., Bergmann, N.W., Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. Ieee T Instrum Meas 61:10 (2012), 2787–2798.
34. [34] Ding, Y., Ceglarek, D., Shi, J., Fault diagnosis of multistage manufacturing processes by using state space approach. J Manuf Sci Eng 124:2 (2002), 313–322.
35. [35] Monostori, L., Prohaszka, J., A step towards intelligent manufacturing: modelling and monitoring of manufacturing processes through artificial neural networks. Cirp Ann Manuf Techn 42:1 (1993), 485–488.
36. [36] Zhou, Z., Chen, Y., Fuh, J., Nee, A., Integrated condition monitoring and fault diagnosis for modern manufacturing systems. Cirp Ann Manuf Techn 49:1 (2000), 387–390.
37. [37] Bastani, K., Kong, Z., Huang, W., Huo, X., Zhou, Y., Fault diagnosis using an enhanced Relevance Vector Machine (RVM) for partially diagnosable multistation assembly processes, Automation Science and Engineering. IEEE Trans 10:1 (2013), 124–136.
38. [38] Suh, J.H., Kumara, S.R., Mysore, S.P., Machinery fault diagnosis and prognosis: application of advanced signal processing techniques. Cirp Ann Manuf Techn 48:1 (1999), 317–320.
39. [39] Jin, J., Shi, J., Automatic feature extraction of waveform signals for in-process diagnostic performance improvement. J Intell Manuf 12:3 (2001), 257–268.
40. [40] Merchawi, N.S., Kumara, S.R., Neural networks in continuous process diagnostics, Artificial Neural Networks for Intelligent Manufacturing. 1994, Springer, 435–461.
41. [41] Li, X., Yao, X., Multi-scale statistical process monitoring in machining, Industrial Electronics. Trans IEEE 52:3 (2005), 924–927.
42. [42] Foster, I., Kesselman, C., The Grid 2: Blueprint for a new computing infrastructure. 2003, Elsevier.
43. [43] Berman, F., Fox, G., Hey, A.J., Grid computing: making the global infrastructure a reality. 2003, John Wiley and sons.
44. [44] Bonomi, F., Milito, R., Zhu, J., Addepalli, S., Fog computing and its role in the internet of things. Proc. Proceedings of the first edition of the MCC workshop on Mobile cloud computing, ACM, 2017, 13–16.
45. [45] Bonomi, F., Milito, R., Natarajan, P., Zhu, J., Fog computing: A platform for internet of things and analytics, Big Data and Internet of Things: A Roadmap for Smart Environments. 2014, Springer, 169–186.
46. [46] Aazam, M., Huh, E.-N., Fog computing and smart gateway based communication for cloud of things, Proc. Future Internet of Things and Cloud (FiCloud). International Conference on, IEEE, 2014, 464–470.
47. [47] Yi, S., Li, C., Li, Q., A survey of fog computing: concepts, applications and issues. Proc. Proceedings of the 2015 Workshop on Mobile Big Data, ACM, 2017, 37–42.
48. [48] Stojmenovic, I., Fog computing: a cloud to the ground support for smart things and machine-to-machine networks. Proc. Telecommunication Networks and Applications Conference (ATNAC), Australasian, IEEE, 2014, 117–122.
49. [49] Stolfo, S.J., Salem, M.B., Keromytis, A.D., Fog computing: mitigating insider data theft attacks in the cloud. Proc. Security and Privacy Workshops (SPW), IEEE Symposium on, IEEE, 2012, 125–128.
50. [50] Vijayaraghavan, A., Sobel, W., Fox, A., Dornfeld, D., Warndorf, P., Improving machine tool interoperability using standardized interface protocols: MT connect. Lab Manuf Sustain, 2008.
51. [51] MTConnect, MTConnect Overview and Protocol. 2015 http://www.mtconnect.org/.
52. [52] Predix, Predix: The Industrial Internet Platform. 2016.
53. [53] Liu, S., Zhang, J.G.L., Smith, D., Grossner, M., Cloud-enabled industrial machine pervasive monitoring system. Proc. of 2014 International Symposium on Flexible AutomationAwaji-Island, Hyogo, Japan, 2014.
54. [54] Li, X., Lim, B., Zhou, J., Huang, S., Phua, S., Shaw, K., et al. Fuzzy neural network modelling for tool wear estimation in dry milling operation. Annual Conference of the Prognostics and Health Management Society, 2009, 1–11.
55. [55] Breiman, L., Random forests. Mach Learn 45:1 (2001), 5–32.