Introduction to Data-Driven Methodologies for Prognostics and Health Management

  • Jay Lee
  • Chao Jin
  • Zongchang Liu
  • Hossein Davari Ardakani


This book chapter gives an overview of prognostics and health management (PHM) methodologies followed by a case study in the development of PHM solutions for wind turbines. Research topics in PHM are identified and commonly used methods are briefly introduced. The case study in wind turbine prognostics has shown in detail how to develop a PHM system for an industrial asset. With the advancement of sensing technologies and computational capability, more and more industrial applications are emerging. Current gaps and future directions in PHM are discussed at the end.


Prognostics and health management Wind energy Data-driven Prognostics 


  1. 1.
    A. Alter, P. Banerjee, P. E. Daugherty, W. Negm, Driving Unconventional Growth through the Industrial Internet of Things, 2014Google Scholar
  2. 2.
    D.O. Gray, D. Rivers, Measuring the Economic Impacts of the NSF Industry/University Cooperative Research Centers Program: A Feasibility Study, 2012Google Scholar
  3. 3.
    J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, D. Siegel, Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech. Syst. Signal Process. 42(1), 314–334 (2014)CrossRefGoogle Scholar
  4. 4.
    M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms (Wiley, 2011)Google Scholar
  5. 5.
    I.H. Witten, E. Frank, Data Mining: Practical Machine learning tools and Techniques (Morgan Kaufmann, 2005)Google Scholar
  6. 6.
    K.P. Murphy, Machine Learning: a Probabilistic Perspective (MIT press, 2012)Google Scholar
  7. 7.
    M. Pecht, R. Jaai, A prognostics and health management roadmap for information and electronics-rich systems. Microelectron. Reliab. 50(3), 317–323 (2010)CrossRefGoogle Scholar
  8. 8.
    Z. Ge, Z. Song, Multivariate Statistical Process Control: Process Monitoring Methods and Applications (Springer Science & Business Media, 2012)Google Scholar
  9. 9.
    C. Jin, A.P. Ompusunggu, Z. Liu, H.D. Ardakani, F. Petre, J. Lee, Envelope analysis on vibration signals for stator winding fault early detection in 3-phase induction motors. Int. J. Progn. Health Manag. 6, 12 (2015)Google Scholar
  10. 10.
    A.K.S. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20(7), 1483–1510 (2006)CrossRefGoogle Scholar
  11. 11.
    R.B. Randall, Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications (John Wiley & Sons, 2011)Google Scholar
  12. 12.
    J.W. Hines, R. Seibert, Technical review of on-line monitoring techniques for performance assessment. State-of-the-Art 1 (2006)Google Scholar
  13. 13.
    G.A. Cherry, Methods for Improving the Reliability of Semiconductor Fault Detection and Diagnosis with Principal Component Analysis, 2006Google Scholar
  14. 14.
    E. Bechhoefer, D. He, P. Dempsey, Gear health threshold setting based on a probability of false alarm, in Proceedings of Annual Conference of the Prognostics and Health Management Society, 2011Google Scholar
  15. 15.
    H. Oh, M.H. Azarian, M. Pecht, Estimation of fan bearing degradation using acoustic emission analysis and Mahalanobis distance, in Proceedings of the Applied Systems Health Management Conference, pp. 1–12, 2011Google Scholar
  16. 16.
    R. Ganesan, A. N. V. Rao, and T. K. Das, A Multiscale Bayesian SPRT Approach for Online Process Monitoring, in IEEE Transactions of Semiconductor Manufacturing, vol. 21.3, pp. 399–412, 2008Google Scholar
  17. 17.
    D. Tax, A. Ypma, R. Duin, Support vector data description applied to machine vibration analysis, in Proceedings of 5th Annual Conference of the Advanced School for Computing and Imaging (Heijen, NL), pp. 398–405, 1999Google Scholar
  18. 18.
    D. He, E. Bechhoefer, Development and validation of bearing diagnostic and prognostic tools using HUMS condition indicators, in Proceedings of 2008 IEEE Aerospace Conference, pp. 1–8, 2008Google Scholar
  19. 19.
    D.J. Cleary, P.E. Cuddihy, A novel approach to aircraft engine anomaly detection and diagnostics, in Proceedings of 2004 IEEE Aerospace Conference, vol. 5, pp. 3468–3475, (2004)Google Scholar
  20. 20.
    W. Yan, F. Xue, Jet engine gas path fault diagnosis using dynamic fusion of multiple classifiers, in Proceedings of 2008 IEEE International Joint Conference on Neural Networks, pp. 1585–1591, 2008Google Scholar
  21. 21.
    L. Yang, J. Lee, Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems. Robot. Comput.-Integr. Manuf. 28(1), 66–74 (2012)CrossRefGoogle Scholar
  22. 22.
    N. Gebraeel, M. Lawley, R. Liu, V. Parmeshwaran, Residual life predictions from vibration-based degradation signals: a neural network approach. Ind. Electron. IEEE Trans. 51(3), 694–700 (2004)CrossRefGoogle Scholar
  23. 23.
    T. Wang, J. Yu, D. Siegel, J. Lee, A similarity-based prognostics approach for remaining useful life estimation of engineered systems, in Proceedings of International Conference on Prognostics and Health Management, pp. 1–6, 2008Google Scholar
  24. 24.
    M.E. Orchard, A Particle Filtering-Based Framework for On-Line Fault Diagnosis and Failure Prognosis (Georgia Institute of Technology)Google Scholar
  25. 25.
    S. Sawyer, K. Rave, Global Wind Report–Annual Market Update 2012, (GWEC, Glob. Wind Energy Council, 2013)Google Scholar
  26. 26.
    U.S. Department of Energy, Wind Power Today 2010, 2010Google Scholar
  27. 27.
    S. Sheng, P.S. Veers, Wind Turbine Drivetrain Condition Monitoring-An Overview (National Renewable Energy Laboratory, 2011)Google Scholar
  28. 28.
    P. Gardner, A. Garrad, L.F. Hansen, A. Tindal, J.I. Cruz, L. Arribas, N. Fichaux, Wind Energy-The Facts Part 1 Technology (EWEA, Garrad Hassan Partners, UK CIEMAT, Spain, 2009)Google Scholar
  29. 29.
    S. Faulstich, B. Hahn, P.J. Tavner, Wind turbine downtime and its importance for offshore deployment. Wind Energy 14(3), 327–337 (2011)CrossRefGoogle Scholar
  30. 30.
    E.R. Lapira, Fault Detection in a Network of Similar Machines Using Clustering Approach, (University of Cincinnati, 2012)Google Scholar
  31. 31.
    D. Siegel, W. Zhao, E. Lapira, M. AbuAli, J. Lee, A comparative study on vibration-based condition monitoring algorithms for wind turbine drive trains. Wind Energy 17(5), 695–714 (2014)CrossRefGoogle Scholar
  32. 32.
    A. Jabłoński, T. Barszcz, M. Bielecka, Automatic validation of vibration signals in wind farm distributed monitoring systems. Measurement 44(10), 1954–1967 (2011)CrossRefGoogle Scholar
  33. 33.
    General Electric, Predix.
  34. 34.
    National Instruments, Big Analog DataTM Solutions.
  35. 35.
    Center for Intelligent Maintenance Systems, Development of Smart Prognostics Agents (WATCHDOG AGENT®).
  36. 36.
    National Instruments, Watchdog AgentTM Prognostics Toolkit for LabVIEW—IMS Center.
  37. 37.
    Applied Materials, Applied TechEdgeTM PrizmTM.
  38. 38.
    CANRIG, RigWatch® Instrumentation and Equipment Condition Monitoring.
  39. 39.
    Y. Chen, J. Lee, Data Quality Assessment Methodology for Improved Prognostics Modeling (University of CIncinnati, Cincinnati, OH, 2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jay Lee
    • 1
  • Chao Jin
    • 1
  • Zongchang Liu
    • 1
  • Hossein Davari Ardakani
    • 1
  1. 1.NSF I/UCRC for Intelligent Maintenance Systems (IMS)University of CincinnatiCincinnatiUSA

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