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Introduction

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Prognostics and Health Management of Engineering Systems

Abstract

Prognostics and health management (PHM) is a new engineering approach that enables real-time health assessment of a system under its actual operating conditions as well as the prediction of its future state based on up-to-date information by incorporating various disciplines including sensing technologies, failure physics, machine learning, modern statistics, and reliability engineering.

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References

  • Ahmadzadeh F, Lundberg J (2013) Remaining useful life prediction of grinding mill liners using an artificial neural network. Miner Eng 53:1–8

    Article  Google Scholar 

  • An D, Choi JH, Kim NH (2013) Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab. Reliab Eng Syst Saf 115:161–169

    Article  Google Scholar 

  • Bayes T, Price R (1763) An Essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S. Philos Trans R Soc Lond 53:370–418. doi:10.1098/rstl.1763.0053

  • Bechhoefer E, Mortom B (2012) Condition monitoring architecture: to reduce total cost of ownership. In: Paper presented at the IEEE prognostics and health management conference, Denver, Colorado, USA, 18–21 June 2012

    Google Scholar 

  • Benkedjouh T, Medjaher K, Zerhouni N et al (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223

    Article  Google Scholar 

  • Bretscher O (ed) (1995) Linear algebra with applications. Prentice Hall, New Jersey

    MATH  Google Scholar 

  • CALCE (1986) Center for advanced life cycle engineering. http://www.calce.umd.edu. Accessed 3 June 2016

  • Callan R, Larder B, Sandiford J (2006) An integrated approach to the development of an intelligent prognostic health management system. In: Paper presented at the IEEE aerospace conference, Big Sky, Montana, USA, 4–11 March 2006

    Google Scholar 

  • Chakraborty K, Mehrotra K, Mohan CK et al (1992) Forecasting the behavior of multivariate time series using neural networks. Neural Netw 5:961–970

    Article  Google Scholar 

  • Cheng S, Azarian MH, Pecht MG (2010) Sensor systems for prognostics and health management. Sensors 10(6):5774–5797

    Article  Google Scholar 

  • Choi JH, An D, Gang J et al (2010) Bayesian approach for parameter estimation in the structural analysis and prognosis. In: Paper presented at the annual conference of the prog-nostics and health management society, Portland, Oregon, 13–16 Oct 2010

    Google Scholar 

  • Christer AH, Wang W, Sharp J (1997) A state space condition monitoring model for furnace erosion prediction and replacement. Eur J Oper Res 101(1):1–14

    Article  MATH  Google Scholar 

  • DoD Instruction (2003) Operation of the defense acquisition system. DoD Instruction 5000.2. May 12, 2003. http://www.acq.osd.mil/dpap/Docs/new/5000.2%2005-12-06.pdf. Accessed 3 June 2016

  • Doucet A, Freitas DN, Gordon NJ (2001) Sequential Monte Carlo methods in practice. Springer, New York

    Book  MATH  Google Scholar 

  • Evans PC, Annunziata M (2012) Industrial internet: pushing the boundaries of minds and machines. GE report, 11.26.2012. http://www.ge.com/docs/chapters/Industrial_Internet.pdf. Accessed 3 June 2016

  • FEMTO-ST (2004) Franche-Comté Electronique, Mécanique, Thermique et Optique - Sciences et Technologies. http://www.femto-st.fr/en. Accessed 3 June 2016

  • Gavin HP (2016) The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems. Available via Duke University. http://people.duke.edu/~hpgavin/ce281/lm.pdf. Accessed 28 May 2016

  • Gray DO, Drew R, George V (2012) Measuring the economic impacts of the NSF industry/university cooperative research centers program: a feasibility study. Final report for industry-university cooperative research center program. https://www.ncsu.edu/iucrc/PDFs/IUCRC_EconImpactFeasibilityReport_Final.pdf. Accessed 3 June 2016

  • Greitzer FL, Hostick CJ, Rhoads RE et al (2001) Determining how to do prognostics, and then determining what to do with it. In: Paper presented at the IEEE systems readiness technology conference, AUTOTESTCON proceedings, Valley Forge, Pennsylvania, USA, 20–23 Aug 2001

    Google Scholar 

  • Hardman W (2004) Mechanical and propulsion systems prognostics: U.S. navy strategy and demonstration. J Miner Metals Mater Soc 56(3):21–27

    Article  Google Scholar 

  • He Y, Lin M. (2012) Research on PHM architecture design with electronic equipment. In: Paper presented at the IEEE conference on prognostics and health management, Beijing, China, 23–25 May 2012

    Google Scholar 

  • IEEE Reliability Society (2011) International conference on prognostics and health management. http://rs.ieee.org. Accessed 3 Jun 2016

  • IMS Center (2001) Center for intelligent maintenance systems. http://www.imscenter.net. Accessed 3 June 2016

  • ISO 13381-1 (2015) Condition monitoring and diagnostics of machines—Prognostics—Part 1: General guidelines. International Standards Organization Available via http://www.iso.org/iso/home/store/catalogue_ics/catalogue_detail_ics.htm?csnumber=51436. Accessed 3 June 2016

  • IVHM (2008) Integrated vehicle health management center. https://www.cranfield.ac.uk/About/People-and-Resources/Schools-institutes-and-research-centres/satm-centres/Integrated-Vehicle-Health-Management-IVHM-Centre. Accessed 3 June 2016

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

    Article  Google Scholar 

  • John Burt Associates Limited (2011) UK offshore commercial air transport helicopter safety record (1981–2010). Oil & Gas UK. http://oilandgasuk.co.uk/wp-content/uploads/2015/05/HS027.pdf. Accessed 3 June 2016

  • Joint Strike Fighter Program Office (2016) Joint strike fighter PHM vision. https://www.phmsociety.org/sites/phmsociety.org/files/PHMvision.pdf. Accessed 3 June 2016

  • Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92(3):401–422

    Article  Google Scholar 

  • Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82:35–45

    Article  Google Scholar 

  • Leão BP, Yoneyama T, Rocha GC (2008) Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit. In: Paper presented at the international conference on prognostics and health management, Denver, Colorado, USA, 6–9 Oct 2008

    Google Scholar 

  • Lee J, Wu F, Zhao W et al (2014) Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech Syst Signal Process 42(1):314–334

    Article  Google Scholar 

  • Li D, Wang W, Ismail F (2013) Enhanced fuzzy-filtered neural networks for material fatigue prognosis. Appl Soft Comput 13(1):283–291

    Article  Google Scholar 

  • Li YG, Nikitsaranont P (2008) Gas path prognostic analysis for an industrial gas turbine. Insight Non-Destruct Testing Condition Monitoring 50(8):428–435

    Article  Google Scholar 

  • Liao L, Köttig F (2014) Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Reliab 63(1):191–207

    Article  Google Scholar 

  • Liao L, Lee J (2010) Design of a reconfigurable prognostics platform for machine tools. Expert Syst Appl 37:240–252. doi:10.1016/j.eswa.2009.05.004

    Article  Google Scholar 

  • Liu A, Dong M, Peng Y (2012) A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods. Mech Syst Signal Process 32:331–348

    Article  Google Scholar 

  • Kao A, Lee J, Edzel E et al (2011) iFactory cloud service platform based on IMS tools and servolution. In: Paper presented at the world congress on engineering asset management, Cincinnati, Ohio, USA, 3–5 Oct 2011

    Google Scholar 

  • Mackay DJC (1998) Introduction to Gaussian processes. NATO ASI Series F Comput Syst Sci 168:133–166

    MATH  Google Scholar 

  • Murakami T, Saigo T, Ohkura Y et al (2002) Development of vehicle health monitoring system (VHMS/WebCARE) for large-sized construction machine. Komatsu’s Technical Report 48(150):15–21

    Google Scholar 

  • Pandey MD, Noortwijk JMV (2004) Gamma process model for time-dependent structural reliability analysis. In: Paper presented at the second international conference on bridge maintenance, safety and management, Kyoto, Japan, 18–22 Oct 2004

    Google Scholar 

  • Paris PC, Erdogan F (1963) A critical analysis of crack propagation laws. ASME J Basic Eng 85:528–534

    Article  Google Scholar 

  • PCoE (2016) Prognostics center of excellence. http://ti.arc.nasa.gov/tech/dash/pcoe. Accessed 3 June 2016

  • Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50(1–4):297–313

    Article  Google Scholar 

  • Pettit CD, Barkhoudarian S, Daumann AG (1999) Reusable rocket engine advanced health management system architecture and technology evaluation—summary. In: Paper presented at the 35th AIAA/ASME/SAE/ASEE joint propulsion conference and exhibit, Los Angeles, California, USA, 20–24 June 1999

    Google Scholar 

  • PHM Society (2009) The prognostics and health management society. http://www.phmsociety.org. Accessed 3 June 2016

  • Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman filter: particle filters for tracking applications. IEEE Aerosp Electron Syst Mag 19(7):37–38

    Article  MATH  Google Scholar 

  • Saha B, Celaya J, Wysocki P et al (2009) Towards prognostics for electronics components. In: Paper presented at the IEEE aerospace conference, Big Sky, Montana, USA, 7–14 March 2009

    Google Scholar 

  • Saxena A, Celaya J, Saha B et al (2009) On applying the prognostic performance metrics. In: Paper presented at the annual conference of the prognostics and health management society, San Diego, California, USA, 27 Sep–1 Oct 2009

    Google Scholar 

  • Scheuren WJ, Caldwell KA, Goodman GA et al (1998) Joint strike fighter prognostics and health management. In: Paper presented at the AIAA joint propulsion conference, Cleveland, Ohio, 12–15 July 1998

    Google Scholar 

  • Seeger M (2004) Gaussian processes for machine learning. Int J Neural Syst 14(2):69–106

    Article  Google Scholar 

  • Sheppard JW, Kaufman MA, Wilmer TJ (2009) IEEE standards for prognostics and health management. Aerospace Electr Syst Mag IEEE 24(9):34–41

    Article  Google Scholar 

  • Si XS, Wang W, Hu CH et al (2011) Remaining useful life estimation—a review on the statistical data driven approaches. Eur J Oper Res 213:1–14

    Article  MathSciNet  Google Scholar 

  • Si XS, Wang W, Hu CH et al (2013) A Wiener process-based degradation model with a re-cursive filter algorithm for remaining useful life estimation. Mech Syst Signal Process 35(1–2):219–237

    Article  Google Scholar 

  • Siegel D, Edzel L, AbuAli M et al (2009) A systematic health monitoring methodology for sparsely sampled machine data. In: Paper presented at the US society for machinery failure prevention technology (MFPT) conference, Dayton, Ohio, 28–30 Apr 2009

    Google Scholar 

  • Siegel D, ZhaoW Lapira E et al (2014) A comparative study on vibration-based condition monitoring algorithms for wind turbine drive trains. Wind Energy 17(5):695–714

    Article  Google Scholar 

  • Sikorska JZ, Melinda H, Lin M (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803–1836

    Article  Google Scholar 

  • Sodemann A, Li Y, Lee J et al (2006) Data-driven surge map modeling for centrifugal air compressors. In: Paper presented at the ASME international mechanical engineering congress and exposition, Chicago, Illinois, 5–10 Nov 2006

    Google Scholar 

  • Space Daily (2003) Prognosis program begins at DARPA. A newspaper report, Space Daily. http://www.spacedaily.com/news/materials-03zv.html. Accessed 3 June 2016

  • Sun B, Zeng S, Kang R et al (2012) Benefits and challenges of system prognostics. IEEE Trans Reliab 61(2):323–335

    Article  Google Scholar 

  • Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Machine Learning Res 1:211–244

    MathSciNet  MATH  Google Scholar 

  • UTC Aerospace Systems (2013) Health and Usage Management System (HUMS). http://utcaerospacesystems.com/cap/systems/sisdocuments/Health%20and%20Usage%20Management%20Systems%20(HUMS)/Health%20and%20Usage%20Management%20Systems%20(HUMS).pdf. Accessed 3 June 2016

  • Vachtsevanos G, Lewis F, Roemer M et al (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken

    Book  Google Scholar 

  • VerWey J (2009) Airplane product strategy during a time of market transition. Boeing: e-newsletter June 2009. http://www.boeingcapital.com/p2p/archive/06.2009. Accessed 3 June 2016

  • Vichare N, Rodgers P, Eveloy V et al (2004) In-situ temperature measurement of a notebook computer—a case study in health and usage monitoring of electronics. IEEE Trans Device Mater Reliab 4(4):658–663

    Article  Google Scholar 

  • Vogl GW, Weiss BA, Donmez MA (2014) Standards related to prognostics and health management (PHM) for manufacturing. National Institute of Standards and Technology. http://dx.doi.org/10.6028/NIST.IR.8012. Accessed 3 June 2016

  • Wang W, Scarf PA, Smith MAJ (2000) On the application of a model of condition based maintenance. J Oper Res Soc 51(11):1218–1227

    Article  MATH  Google Scholar 

  • Wang H, Lee J, Ueda T et al (2007) Engine health assessment and prediction using the group method of data handling and the method of match matrix—autoregressive moving average. In: Paper presented at the ASME Turbo Expo, Montreal, Canada, 14–17 May 2007

    Google Scholar 

  • Yin S, Ding S, Zhou D (2016) Diagnosis and prognosis for complicated industrial systems—Part I. IEEE Trans Industr Electron 63(4):2501–2505

    Article  Google Scholar 

  • Zio E, Maio FD (2010) A Data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliab Eng Syst Saf 95:49–57

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA

    Nam-Ho Kim

  2. Daegyeong Division/Aircraft System Technology Group, Korea Institute of Industrial Technology, Yeongcheon-si, Gyeongbuk-do, Republic of Korea

    Dawn An

  3. Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City, Kyonggi-do, Republic of Korea

    Joo-Ho Choi

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  1. Nam-Ho Kim
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  2. Dawn An
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  3. Joo-Ho Choi
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Correspondence to Nam-Ho Kim .

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Kim, NH., An, D., Choi, JH. (2017). Introduction. In: Prognostics and Health Management of Engineering Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-44742-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-44742-1_1

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