Skip to main content
SpringerLink
Log in

Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications

  • Chapter
  • First Online:
Engineering Asset Management Review

Part of the book series: Engineering Asset Management Review

Abstract

Over recent years, a significant amount of research has been dedicated to the development of Prognostics and Health Management (PHM) models. However, less attention is paid towards implementing the developed models to real-world applications to serve the needs of industry. In order to successfully implement a PHM system, a systematic approach is required to deploy the developed analytic tools (algorithms, software and agents) using a scalable hardware platform. In this paper, different PHM deployment platforms including stand-alone PC, embedded and cloud-based platforms are benchmarked. Then, a unified strategy for deploying the developed PHM tools using each of these platforms is presented. A smart deployment platform selection method using Quality Function Deployment (QFD) is also introduced. Following that, several case studies from different applications are provided as examples to demonstrate the capabilities and limitations of each deployment platform.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Sikorska, J.Z., Hodkiewicz, M., Ma, L.: Prognostic modelling options for remaining useful life estimation by industry. Mech. Syst. Signal Process. 25(5), 1803–1836 (2011)

    Article  ADS  Google Scholar 

  2. Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20(7), 1483–1510 (2006)

    Article  ADS  Google Scholar 

  3. Nandi, S., Toliyat, H.A.: Condition monitoring and fault diagnosis of electrical machines-a review. In: Industry Applications Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the 1999 IEEE, Phoenix, AZ (1999)

    Google Scholar 

  4. Heng, A., et al.: Rotating machinery prognostics: state of the art, challenges and opportunities. Mech. Syst. Signal Process. 23(3), 724–739 (2009)

    Article  ADS  Google Scholar 

  5. Zhang, J., Lee, J.: A review on prognostics and health monitoring of Li-ion battery. J. Power Sources 196(15), 6007–6014 (2011)

    Article  CAS  ADS  Google Scholar 

  6. Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18(2), 199–221 (2004)

    Article  ADS  Google Scholar 

  7. Scarf, P.A.: On the application of mathematical models in maintenance. Eur. J. Oper. Res. 99(3), 493–506 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  8. Ge, M., et al.: Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mech. Syst. Signal. Process. 18(1), 143–159 (2004)

    Article  ADS  Google Scholar 

  9. Wu, S., Chow, T.W.S.: Induction machine fault detection using SOM-based RBF neural networks. IEEE Trans. Ind. Electron. 51(1), 183–194 (2004)

    Article  Google Scholar 

  10. Li, Z., et al.: Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery. Mech. Syst. Signal Process. 19(2), 329–339 (2005)

    Article  ADS  Google Scholar 

  11. Qu, R.: An Implementation of a remote diagnostic system on rotational machines. Struct. Health Monit. 5(2), 185–193 (2006)

    Article  Google Scholar 

  12. Chen, Z., Lee, J., Qiu, H.: Intelligent infotronics system platform for remote monitoring and E-maintenance. Int. J. Agil. Manuf. 8(1), 3–11 (2005)

    Google Scholar 

  13. Holm-Hansen, B.T., Gao, R.X.: Smart bearing utilizing embedded sensors: design considerations. In: Proceedings of the SPIE 4th International Symposium on Smart Structures and Materials, San Diego, CA (1997)

    Google Scholar 

  14. Chen, Z.S., Yang, Y.M., Hu, Z.: A technical framework and roadmap of embedded diagnostics and prognostics for complex mechanical systems in prognostics and health management systems. IEEE Trans. Reliab. 61(2), 314–322 (2012)

    Article  CAS  MathSciNet  Google Scholar 

  15. Sankavaram, C., et al.: A prognostic framework for health management of coupled systems. In: IEEE Conference on Prognostics and Health Management (PHM), (2011)

    Google Scholar 

  16. Bechhoefer, E., Fang, A.: Algorithms for embedded PHM. In: IEEE Conference on Prognostics and Health Management (PHM), IEEE, Denver (2012)

    Google Scholar 

  17. Siyambalapitiya, D.J.T., McLaren, P.G.: Reliability improvement and economic benefits of on-line monitoring systems for large induction machines. IEEE Trans. Ind. Appl. 26(6), 1018–1025 (1990)

    Article  Google Scholar 

  18. Mao, H., et al.: Wukong: a cloud-oriented file service for mobile Internet devices. J. Parallel Distributed Comput. 72(2), 171–184 (2012)

    Article  Google Scholar 

  19. Dougherty, B., White, J., Schmidt, D.C.: Model-driven auto-scaling of green cloud computing infrastructure. Futur. Gener. Comput. Syst. 28(2), 371–378 (2012)

    Article  Google Scholar 

  20. Mell, P.: What's special about cloud security? IT Professional 14(4), 6–8 (2012)

    Article  Google Scholar 

  21. Rodriguez-Martinez, M., Seguel, J., Greer, M.: Open source cloud computing tools: a case study with a weather application. In: IEEE 3rd International Conference on Cloud Computing (CLOUD), Miami, FL (2010)

    Google Scholar 

  22. Huang, D., et al.: Secure data processing framework for mobile cloud computing. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, Shanghai, China (2011)

    Google Scholar 

  23. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J Internet Serv. Appl. 1(1), 7–18 (2010)

    Article  CAS  Google Scholar 

  24. Pecht, M., Jaai, R.: A prognostics and health management roadmap for information and electronics-rich systems. Microelectron Reliab. 50(3), 317–323 (2010)

    Article  Google Scholar 

  25. Julka, N., et al.: Making use of prognostics health management information for aerospace spare components logistics network optimisation. Comput. Ind. 62(6), 613–622 (2011)

    Article  Google Scholar 

  26. Srivastava, A.N., Schumann, J.: The case for software health management. In: IEEE Fourth International Conference on Space Mission Challenges for Information Technology (SMC-IT), Palo Alto, CA (2011)

    Google Scholar 

  27. Lee, J., et al.: Informatics platform for designing and deploying e-manufacturing systems. In: Wang, L., Nee, A.Y.C. (eds.) Collaborative Design and Planning for Digital Manufacturing, pp. 1–35. Springer, London (2009)

    Google Scholar 

  28. Revelle, J.B., Moran, J.W. Cox, C.A.: The QFD Handbook. Wiley, New York (1998)

    Google Scholar 

  29. Liao, L., Lee, J.: Design of a reconfigurable prognostics platform for machine tools. Expert Syst. Appl. 37(1), 240–252 (2010)

    Article  Google Scholar 

  30. Chun, B.-G., Maniatis, P.: Augmented smartphone applications through clone cloud execution. In: Proceedings of the 8th Workshop on Hot Topics in Operating Systems (HotOS), Monte Verita (2009)

    Google Scholar 

  31. Kao, A., et al.: iFactory Cloud service platform based on IMS tools and servo-lution. In: World Congress on Engineering Asset Management (WCEAM), Cincinnati (2011)

    Google Scholar 

  32. Lee, J., et al.: Trends and recent advances on cloud-based machinery monitoring and prognostics. In: 2nd International Conference on Pervasive Embedded Computing and Communication Systems, Rome (2012)

    Google Scholar 

  33. Govers, C.P.M.: What and how about quality function deployment (QFD). Int. J. Prod. Econ. 46–47, 575–585 (1996)

    Article  Google Scholar 

  34. Siegel, D., et al.: A systematic health monitoring methodology for sparsely sampled machine data. In: Machine Failure and Prevention Conference (MFPT). Dayton (2009)

    Google Scholar 

  35. Sodemann, A., et al.: Data-driven surge map modeling for centrifugal air compressors. In: ASME International Mechanical Engineering Congress and Exposition (IMECE), Chicago (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    Jay Lee, Hossein Davari Ardakani, Hung-An Kao, David Siegel, Mohammad Rezvani & Yan Chen

Authors
  1. Jay Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Davari Ardakani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hung-An Kao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Siegel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Rezvani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jay Lee .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Lee, J., Ardakani, H.D., Kao, HA., Siegel, D., Rezvani, M., Chen, Y. (2015). Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications. In: Engineering Asset Management Review. Springer, London. https://doi.org/10.1007/8663_2015_2

Download citation

  • DOI: https://doi.org/10.1007/8663_2015_2

  • Published:

  • Publisher Name: Springer, London

Publish with us

Policies and ethics

Search

Navigation