Skip to main content
  • 1923 Accesses

Abstract

The word prognosis comes from the Greek prognostikos (of knowledge beforehand). It combines pro (before) and gnosis (a knowing). Hippocrates used the word prognosis, much as we do today, to mean a foretelling of the course of a disease. In the field of engineering systems health management, prognosis is regarded as science and often called prognostics.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Atiya A, El-Shoura S, Shaheen S, El-Sherif M (1999) A comparison between neural-network forecasting techniques—case study: river flow forecasting. IEEE Trans Neural Networks 10(2):402–409

    Article  Google Scholar 

  • Bezdek IC, Pal SK (eds) (1992) Fuzzy models for pattern recognition: methods that search for structure in data. IEEE Press, New York

    Google Scholar 

  • Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco

    Google Scholar 

  • Christer AH (1976) Innovative decision making. In: Brown KC, White DJ (eds) Proceedings of the NATO conference on role and effectiveness of theory of decision practice. Hodder and Stoughton, UK, pp 368–377

    Google Scholar 

  • Cristianini N, Shawe-Taylor NJ (2000) An introduction to support vector machines. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Connor J, Martin R, Atlas L (1994) Recurrent neural networks and robust time-series prediction. IEEE Trans Neural Networks 5(2):240–254

    Article  Google Scholar 

  • Drexel M, Ginsberg JH (2001) Mode isolation: a new algorithm for modal parameter identification. J Acoust Soc Am 110(3):1371–1378

    Article  Google Scholar 

  • Engel SJ, Gilmartin BJ, Bongort K, Hess A (2000) Prognostics, the real issues involved with predicting life remaining. Aerosp Conf Proc 6:457–469

    Google Scholar 

  • Friedman J (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67

    Article  MathSciNet  MATH  Google Scholar 

  • Gertler JJ (1998) Fault detection and diagnosis in engineering systems. Marcel Dekker, Inc, NewYork

    Google Scholar 

  • Groer PG (2000) Analysis of time-to-failure with a Weibull model. In: Proceeding of the Maintenance and Reliability Conference, Knoxville

    Google Scholar 

  • Gun SR (1998) Support vector machine for classification and regression. Technical Report, University of Southampton, UK

    Google Scholar 

  • Haykin S (1994) Neural networks—a comprehensive foundation. Macmillan College Pub. Co, New York

    MATH  Google Scholar 

  • Heicht-Nielsen R (1990) Neurocomputing. Addison-Wesley Publishing Co., Redwood City

    Google Scholar 

  • Hush DR, Horne BG (1993) Progress in supervised neural networks. IEEE signal processing magazine. 10(1):8–39

    Google Scholar 

  • Husmeier D (1999) Neural networks for conditional probability estimation: forecasting beyond point prediction. Springer, London

    Book  Google Scholar 

  • Jang J, Sun C, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice-Hall Inc., Englewood Cliffs

    Google Scholar 

  • Kallberg KT, Rossi A (2007) Remaining useful life. www.italamericon.com/usefullife.htm

  • Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205

    Article  Google Scholar 

  • Kotzalas M, Harris T (2001) Fatigue failure progression in ball bearings. Trans ASME J Tribol 123:238–242

    Article  Google Scholar 

  • Lewicki (2001) Gear crack propagation pat studies—guideline for ultrasave design, NASA/TM-2001-211073

    Google Scholar 

  • Lewis L (1986) Optimal estimation: with an introduction to stochastic control theory. Wiley, New York

    MATH  Google Scholar 

  • Li X, Sun C, Gong D (2005) Application of support vector machine and similar day method for load forecasting. LNCS 3611:602–609

    Google Scholar 

  • Lundberg G, Palmagen A (1947) Dynamic capacity of rolling bearings. Acta Polytechnica Mechanical Engineering Series 1, Royal Swedish Academy of Engineering Sciences

    Google Scholar 

  • Luo J, Namburu M, Pattipati K, Qiao L, Kawamoto M, Chigusa S (2003) Model-based techniques. In: Proceedings of IEEE AUTOTESTCON, pp 330–340

    Google Scholar 

  • Mohandes MA, Halawani TO, Rehman S, Hussain AA (2004) Support vector for wind speed prediction. Renewable Energy 29:939–947

    Article  Google Scholar 

  • Muller KR, Smola AJ, Scholkopf B, Kohlmorgen J, Vapnik V (1997) Predicting time series with support vector machine. In: Proceedings of the international conference artificial neural network

    Google Scholar 

  • Pourahmadi M (2001) Foundations of time-series analysis and prediction theory. Wiley, New York

    MATH  Google Scholar 

  • Rao T (1981) On the theory of bilinear models. J Roy Stat Soc B 43:244–255

    MathSciNet  MATH  Google Scholar 

  • Roemer M, Kacprzynsky G (2001) Development of diagnostic and prognostic technologies for aerospace health management application. In: IEEE aerospace conference, Montana

    Google Scholar 

  • Schomig A, Rose O (2003) On the suitability of the Weibull distribution for the approximation of machine failure. In: Proceeding of the industrial engineering research conference, Portland

    Google Scholar 

  • Tong H, Lim S (1980) Threshold autoregression, limited cycles and cyclical data. J Roy Stat Soc B 42:245–292

    MATH  Google Scholar 

  • Tse P, Atherton D (1999) Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. J Vib Acoust 121:355–362

    Article  Google Scholar 

  • Vachtsevanos G, Lewis F, Roemer M, Hess A, Wu B (2006) Intelligent fault diagnosis and prognosis for engineering system. Wiley, New Jersey

    Book  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  • Vukovic P (2001) One-step ahead predictive fuzzy controller. Fuzzy Sets Syst 122:107–115

    Article  MathSciNet  MATH  Google Scholar 

  • Wang W (2007) A two-stage prognosis model in condition based maintenance. Eur J Oper Res 182:1177–1187

    Article  MATH  Google Scholar 

  • Wang W, Ismail F, Golnaraghi F (2001) Assessment of gear damage monitoring techniques using vibration measurements. Mech Syst Signal Process 15:905–922

    Article  Google Scholar 

  • Wang W, Ismail F, Golnaraghi F (2007) A neuro-fuzzy approach to gear system monitoring. IEEE Trans Fuzzy Syst (in press)

    Google Scholar 

  • Wu CH, Ho JM, Lee DT (2004) Travel-time prediction with support vector regression. IEEE Trans. Intell Transp Syst 5(4):276–281

    Article  Google Scholar 

  • Yang J, Zhang Y (2005) Application research of support vector machines in condition trend prediction of mechanical equipment. Lect Notes Comput Sci 3498:857–864

    Article  MATH  Google Scholar 

  • Yen J, Langari R, Zadeh LA (1995) Industrial applications of fuzzy logic and intelligent systems. IEEE Press, New York

    Google Scholar 

  • Yu WK, Harris T (2001) A new stress-based fatigue life model for ball bearings. Tribol Trans 44(1):11–18

    Article  Google Scholar 

  • Zadeh L (1965) Fuzzy set. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore and Science Press, Beijing, China

About this chapter

Cite this chapter

Niu, G. (2017). Science of Prognostics. In: Data-Driven Technology for Engineering Systems Health Management. Springer, Singapore. https://doi.org/10.1007/978-981-10-2032-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2032-2_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2031-5

  • Online ISBN: 978-981-10-2032-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics