Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
A. Alter, P. Banerjee, P. E. Daugherty, W. Negm, Driving Unconventional Growth through the Industrial Internet of Things, 2014
D.O. Gray, D. Rivers, Measuring the Economic Impacts of the NSF Industry/University Cooperative Research Centers Program: A Feasibility Study, 2012
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)
M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms (Wiley, 2011)
I.H. Witten, E. Frank, Data Mining: Practical Machine learning tools and Techniques (Morgan Kaufmann, 2005)
K.P. Murphy, Machine Learning: a Probabilistic Perspective (MIT press, 2012)
M. Pecht, R. Jaai, A prognostics and health management roadmap for information and electronics-rich systems. Microelectron. Reliab. 50(3), 317–323 (2010)
Z. Ge, Z. Song, Multivariate Statistical Process Control: Process Monitoring Methods and Applications (Springer Science & Business Media, 2012)
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)
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)
R.B. Randall, Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications (John Wiley & Sons, 2011)
J.W. Hines, R. Seibert, Technical review of on-line monitoring techniques for performance assessment. State-of-the-Art 1 (2006)
G.A. Cherry, Methods for Improving the Reliability of Semiconductor Fault Detection and Diagnosis with Principal Component Analysis, 2006
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, 2011
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, 2011
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, 2008
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, 1999
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, 2008
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)
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, 2008
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)
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)
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, 2008
M.E. Orchard, A Particle Filtering-Based Framework for On-Line Fault Diagnosis and Failure Prognosis (Georgia Institute of Technology)
S. Sawyer, K. Rave, Global Wind Report–Annual Market Update 2012, (GWEC, Glob. Wind Energy Council, 2013)
U.S. Department of Energy, Wind Power Today 2010, 2010
S. Sheng, P.S. Veers, Wind Turbine Drivetrain Condition Monitoring-An Overview (National Renewable Energy Laboratory, 2011)
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)
S. Faulstich, B. Hahn, P.J. Tavner, Wind turbine downtime and its importance for offshore deployment. Wind Energy 14(3), 327–337 (2011)
E.R. Lapira, Fault Detection in a Network of Similar Machines Using Clustering Approach, (University of Cincinnati, 2012)
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)
A. Jabłoński, T. Barszcz, M. Bielecka, Automatic validation of vibration signals in wind farm distributed monitoring systems. Measurement 44(10), 1954–1967 (2011)
General Electric, Predix. https://www.ge.com/digital/predix
National Instruments, Big Analog DataTM Solutions. http://www.ni.com/white-paper/14667/en/
Center for Intelligent Maintenance Systems, Development of Smart Prognostics Agents (WATCHDOG AGENT®). http://www.imscenter.net/front-page/Resources/WD.pdf
National Instruments, Watchdog AgentTM Prognostics Toolkit for LabVIEW—IMS Center. http://sine.ni.com/nips/cds/view/p/lang/en/nid/210191
Applied Materials, Applied TechEdgeTM PrizmTM. http://www.appliedmaterials.com/media/documents/techedge-prizm-overview
CANRIG, RigWatch® Instrumentation and Equipment Condition Monitoring. http://www.canrigdrillingtechnology.com/rigwatch.php
Y. Chen, J. Lee, Data Quality Assessment Methodology for Improved Prognostics Modeling (University of CIncinnati, Cincinnati, OH, 2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Lee, J., Jin, C., Liu, Z., Davari Ardakani, H. (2017). Introduction to Data-Driven Methodologies for Prognostics and Health Management. In: Ekwaro-Osire, S., Gonçalves, A., Alemayehu, F. (eds) Probabilistic Prognostics and Health Management of Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-55852-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-55852-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55851-6
Online ISBN: 978-3-319-55852-3
eBook Packages: EnergyEnergy (R0)