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Intelligent equipment design assisted by Cognitive Internet of Things and industrial big data

  • Jiafu Wan
  • Jiapeng Li
  • Qingsong HuaEmail author
  • Antonio Celesti
  • Zhongren Wang
S.I. : Cognitive Computing for Intelligent Application and Service

Abstract

In recent years, the development of emerging technologies has brought about a new era of industrial reform. The current industrial revolution will deeply integrate the new generation of information technology with modern manufacturing industry and production servicing businesses to promote transformation and upgrading. As it is the foundation of the manufacturing industry, intelligent equipment plays an important role in the reform. In this paper, we propose an innovative design method to help design intelligent equipment. Firstly, referring to the architecture of the Cognitive Internet of Things (CIoT) and industrial big data, we proposed the architecture of the method and defined the different layers to process the data. Then, for the acquired external data, we put forward an algorithm which was combined with the technology of CIoT and industrial big data, to help designers analyze and make decisions. Finally, we verified the validity and feasibility of this method through a case study. The results showed that this method could effectively mine the deep information of intelligent equipment and provide more valuable information about design-assisting designers in designing better intelligent equipment.

Keywords

Cognitive Internet of Things Industrial big data Intelligent equipment 

Notes

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (No. 2017YFE0101000), the Science and Technology Program of Guangzhou, China (No. 201802030005), the Joint Fund of the National Natural Science Foundation of China and Guangdong Province (No. U180120020), the Key Program of Natural Science Foundation of Guangdong Province (No. 2017B030311008), and the Fundamental Research Funds for the Central Universities (No. x2jqD2170480).

References

  1. 1.
    Naimi AI, Westreich DJ (2013) Big data: a revolution that will transform how we live, work, and think. Math Comput Educ 47(17):181–183Google Scholar
  2. 2.
    Lee J (2015) Smart factory systems. Informatik-Spektrum 38(3):230–235CrossRefGoogle Scholar
  3. 3.
    Zhang Y, Zhang D, Hassan MM, Alamri A, Peng L (2015) CADRE: cloud-assisted drug recommendation service for online pharmacies. Mobile Netw Appl 20(3):348–355CrossRefGoogle Scholar
  4. 4.
    Zhang Y, Chen M, Huang D, Wu D, Li Y (2016) iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Gener Comput Syst 66:30–35CrossRefGoogle Scholar
  5. 5.
    Zhang Y, Chen M, Mao S, Hu L (2014) CAP: community activity prediction based on big data analysis. Netw IEEE 28(4):52–57CrossRefGoogle Scholar
  6. 6.
    Zhang Y, Tu Z, Wang Q (2017) TempoRec: temporal-topic based recommender for social network services. Mobile Netw Appl 4:1–10Google Scholar
  7. 7.
    Wan J, Yin B, Li D, Celesti A, Tao F, Hua Q (2018) An ontology-based resource reconfiguration method for manufacturing cyber-physical systems. IEEE/ASME Trans Mechatron.  https://doi.org/10.1109/TMECH.2018.2814784 CrossRefGoogle Scholar
  8. 8.
    Wan J, Chen B, Imran M, Tao F, Li D, Liu C, Ahmad S (2018) Toward dynamic resources management for IoT-based manufacturing. IEEE Commun Mag 56(2):52–59CrossRefGoogle Scholar
  9. 9.
    Li X, Li D, Wan J, Liu C, Imran M (2018) Adaptive transmission optimization in SDN-based industrial internet of things with edge computing. IEEE Internet Things J 5(3):1351–1360CrossRefGoogle Scholar
  10. 10.
    Wan J, Chen B, Wang S, Xia M, Li D, Liu C (2018) Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans Ind Inform.  https://doi.org/10.1109/tii.2018.2818932 CrossRefGoogle Scholar
  11. 11.
    Wan J, Tang S, Shu Z, Li D, Wang S, Imran M, Vasilakos AV (2016) Software-defined industrial internet of things in the context of industry 4.0. IEEE Sens J 16(20):7373–7380CrossRefGoogle Scholar
  12. 12.
    Shu Z, Wan J, Lin J, Wang S, Li D, Rho S, Yang C (2016) Traffic engineering in software-defined networking: measurement and management. IEEE Access 4:3246–3256CrossRefGoogle Scholar
  13. 13.
    Wan J, Tang S, Li D, Wang S, Liu C, Abbas H, Vasilakos AV (2017) A manufacturing big data solution for active preventive maintenance. IEEE Trans Ind Inf 13(4):2039–2047CrossRefGoogle Scholar
  14. 14.
    Wan J, Tang S, Hua Q, Li D, Liu C, Lloret J (2018) Context-aware cloud robotics for material handling in cognitive industrial internet of things. IEEE Internet Things J 5(4):2272–2281CrossRefGoogle Scholar
  15. 15.
    Feng Y, Zhang S, Gao Y, Cheng J, Tan J (2016) Intelligent push method of CNC design knowledge based on feature semantic analysis. Integr Manuf Syst 22(1):189–201Google Scholar
  16. 16.
    Guo CG, Wang PJ, Peng T, Liu YX (2011) Optimization design of CNC machine tool spindle based on genetic algorithm. J Northeast Univ 32(6):850–853Google Scholar
  17. 17.
    Huo D, Cheng K, Wardle F (2010) A holistic integrated dynamic design and modelling approach applied to the development of ultraprecision micro-milling machines. Int J Mach Tools Manuf 50(4):335–343CrossRefGoogle Scholar
  18. 18.
    Kapoor V, Tak SS (2005) Fuzzy application to the analytic hierarchy process for robot selection. Kluwer Academic Publishers, DordrechtzbMATHGoogle Scholar
  19. 19.
    Li N, Wang L (2012) Mechanism-parameters design method of an amphibious transformable robot based on multi-objective genetic algorithm. J Mech Eng 48(17):10CrossRefGoogle Scholar
  20. 20.
    Tsai CY, Chang CA (2003) Fuzzy neural networks for intelligent design retrieval using associative manufacturing features. J Intell Manuf 14(2):183–195MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yang CJ, Chen JL (2011) Accelerating preliminary eco-innovation design for products that integrates case-based reasoning and TRIZ method. J Clean Prod 19(9–10):998–1006CrossRefGoogle Scholar
  22. 22.
    Chang X, Terpenny J (2009) Ontology-based data integration and decision support for product e-Design. Robot Comput Integr Manuf 25(6):863–870CrossRefGoogle Scholar
  23. 23.
    Huang HZ, Liu Y, Li Y, Xue L, Wang Z (2013) New evaluation methods for conceptual design selection using computational intelligence techniques. J Mech Sci Technol 27(3):733–746CrossRefGoogle Scholar
  24. 24.
    Lin C, Yan LI, Wen-Qiang LI, Chen J (2013) Development of a computer-aided product innovation and design system—CAIP. Comput Integr Manuf Syst 19(2):319–329Google Scholar
  25. 25.
    Zhan P, Jayaram U, Kim OJ, Zhu L (2010) Knowledge representation and ontology mapping methods for product data in engineering applications. J Comput Inf Sci Eng 10(2):699–715CrossRefGoogle Scholar
  26. 26.
    Wan J, Zhang D, Sun Y, Zou C, Zou C, Cai H (2014) VCMIA: a novel architecture for integrating vehicular cyber-physical systems and mobile cloud computing. Mobile Netw Appl 19(2):153–160CrossRefGoogle Scholar
  27. 27.
    Liu J, Wan J, Zeng B, Wang Q, Song H, Qiu M (2017) A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun Mag 55(7):94–100CrossRefGoogle Scholar
  28. 28.
    Chen B, Wan J, Shu L, Li P, Mukherjee M, Yin B (2018) Smart factory of industry 4.0: key technologies, application case, and challenges. IEEE Access 6:6505–6519CrossRefGoogle Scholar
  29. 29.
    Xia M, Li T, Zhang Y, Silva CWD (2016) Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing. Comput Netw 101:5–18CrossRefGoogle Scholar
  30. 30.
    Kong L, Zhang D, He Z, Xiang Q, Wan J, Tao M (2016) Embracing big data with compressive sensing: a green approach in industrial wireless networks. IEEE Commun Mag 54(10):53–59CrossRefGoogle Scholar
  31. 31.
    Wu Q, Ding G, Xu Y, Feng S, Du Z, Wang J, Long K (2014) Cognitive Internet of Things: a new paradigm beyond connection. IEEE Internet Things J 1(2):129–143CrossRefGoogle Scholar
  32. 32.
    Williams JW, Aggour KS, Interrante J, Mchugh J, Pool E (2015) Bridging high velocity and high volume industrial big data through distributed in-memory storage and analytics. In: IEEE international conference on big data, 2015, pp 932–941Google Scholar
  33. 33.
    Grossmann D, Bender K, Danzer B (2008) OPC UA based field device integration. In: Sice conference, 2008, pp 933–938Google Scholar
  34. 34.
    Feng JH, Guo-Liang LI, Feng JH (2015) A survey on crowdsourcing. Chin J Comput 38:1713Google Scholar
  35. 35.
    Chang PC, Fan CY, Dzan WY (2010) A CBR-based fuzzy decision tree approach for database classification. Expert Syst Appl 37(1):214–225CrossRefGoogle Scholar
  36. 36.
    Wang Q, Bai N, Bai J, Jia C (2013) TRIZ routes the solving process of innovation problem. Duke Math J 18(2):321–330Google Scholar
  37. 37.
    Liao SH (2005) Expert system methodologies and applications: a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103MathSciNetCrossRefGoogle Scholar
  38. 38.
    Liu SX (2016) Innovation design: made in china 2025. Design Manag Rev 27(1):52–58Google Scholar
  39. 39.
    Wan J, Tang S, Li D, Imran M, Zhang C, Liu C, Pang Z (2018) Reconfigurable smart factory for drug packing in healthcare industry 4.0. In: IEEE transactions on industrial informatics (to be publihsed).  https://doi.org/10.1109/tii.2018.2843811
  40. 40.
    Adhikari A, Singh S, Dutta A, Dutta B (2015) A novel information theoretic approach for finding semantic similarity in WordNet. In: TENCON 2015 - 2015 IEEE region 10 conference, pp. 1–6Google Scholar
  41. 41.
    Langone R, Agudelo OM, Moor BD, Suykens JAK (2014) Incremental kernel spectral clustering for online learning of non-stationary data. Neurocomputing 139:246–260CrossRefGoogle Scholar
  42. 42.
    Zhu X, Goldberg AB, Brachman R, Dietterich T (2009) Introduction to semi-supervised learning. Morgan and Claypool PublishersGoogle Scholar
  43. 43.
    Zheng Z, Webb GI (2000) Lazy learning of Bayesian rules. Mach Learn 41(1):53–84CrossRefGoogle Scholar
  44. 44.
    Wu HC, Luk RWP, Wong KF, Kwok KL (2008) Interpreting TF-IDF term weights as making relevance decisions. ACM Trans Inf Syst 26(3):55–59CrossRefGoogle Scholar
  45. 45.
    Zhao YY, Bing Q, Liu T (2010) Sentiment analysis. J Softw 21(8):1834–1848CrossRefGoogle Scholar
  46. 46.
    Zhou JS, Dai XY, Yin CY, Chen JJ (2006) Automatic recognition of chinese organization name based on cascaded conditional random fields. Acta Electron Sin 34(5):804–809Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Mechanical and Electrical EngineeringQingdao UniversityQingdaoChina
  3. 3.Department of EngineeringUniversity of MessinaMessinaItaly
  4. 4.School of Mechanical and Automotive EngineeringHubei University of Arts and ScienceXiangyangChina

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