Skip to main content
SpringerLink
Log in

Corrosion Diagnostic and Prognostic Technologies

  • Chapter
  • First Online:
Corrosion Processes

Part of the book series: Structural Integrity ((STIN,volume 13))

Abstract

This chapter addresses the development and utility of fundamental diagnostic and prognostic algorithms to assist in early detection of corrosion initiation and progression so that immediate remediation can be taken to avoid further structural deterioration while limiting significantly repair and replacement costs. Corrosion, in its different stages, is a significant challenge affecting the operational integrity of a vast variety of equipment and processes. Corrosion prevention costs are amounting to billions of dollars each year. As complex equipment age, exposure to corrosion processes is increasing at a substantial and alarming rate contributing to equipment degradation and leading to failure modes. Major efforts have been underway over the past years to develop and implement corrosion prevention and protection materials/processes to extent the useful life of critical equipment/facilities preventing rapid deterioration and retirement. Early corrosion detection is urgently required to warn the operator/maintainer of impending detrimental events that endanger the integrity and life of critical aerospace and industrial processes exposed to corrosive environments. Accurate prediction of the growth of corrosion states is an essential component of the architecture aiming to provide estimates of the time remaining for remediation while the system/process is required to complete a current task or mission. The enabling technologies build upon the sensing modalities, corrosion modeling tools and methods detailed in previous chapters. Corrosion modeling has been addressed over the past years from multiple investigators on behalf of government agencies and industry (see Chapter on Corrosion Modeling). We take advantage of these efforts to formulate the corrosion diagnostic and prognostic algorithms. We use case studies and examples illustrating the theoretical developments.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

  1. López De La Cruz J, Lindelauf R, Koene L, Gutiérrez MA (2007) Stochastic approach to the spatial analysis of pitting corrosion and pit interaction. Electrochem Commun 9(2):325–330

    Google Scholar 

  2. Forsyth DS, Komorwoski JP (2000) The role of data fusion in NDE for aging aircraft. SPIE Aging Aircr Airpt Aerosp Hardware IV 3994:6

    Google Scholar 

  3. Brown DW, Connolly RJ, Laskowski B, Garvan M, Li H, Agarwala VS, Vachtsevanos G (2014) A novel linear polarization resistance corrosion sensing methodology for aircraft structure. In: Annual conference of the Prognostics and Health Management Society, vol 5, no 33

    Google Scholar 

  4. Brown D, Darr D, Morse J, Laskowski B (2012) Real-time corrosion monitoring of aircraft structures with prognostic applications. In: Annual conference of the prognostics and health management society, Mineapolis, MN, USA, Sept 23–27

    Google Scholar 

  5. McAdam G, Newman PJ, McKenzie I, Davis C, Hinton BR (2005) Fiber optic sensors for detection of corrosion within aircraft. Struct Health Monit 4:47–56

    Article  Google Scholar 

  6. Vachtsevanos G, Lewis F, Roemer M, Hess A, Wu B (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken

    Book  Google Scholar 

  7. Orchard et al. (2005) A particle filtering framework for failure prognosis. In: World Tribology Congress III, Washington, D.C., Rep. WTC2005-64005

    Google Scholar 

  8. Kim H, Drake BL, Park H (2006) Adaptive nonlinear discriminant analysis by regularized minimum squared errors. IEEE Trans Knowl Data Eng 18(5)

    Google Scholar 

  9. Park C, Park H (2005) Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition. SIAM J Matrix Anal Appl 27-1

    Google Scholar 

  10. Wu B, Saxena A, Khawaja TS, Patrick R, Vachtsevanos G, Sparis P (2004) Data analysis and feature selection for fault diagnosis of helicopter planetary gears. IEEE Autotestcon

    Google Scholar 

  11. Wu B, Saxena A, Patrick R, Vachtsevanos G (2005) Vibration monitoring for fault diagnosis of helicopter planetary gears. IFAC Proc Vol (IFAC-Papersonline) 16:755–760

    Article  Google Scholar 

  12. Orchard M (2007) A particle filtering-based framework for on-line fault diagnosis and failure prognosis. Ph.D. Thesis, Department of Electrical and Computer Engineering, Georgia Institute of Technology

    Google Scholar 

  13. Brown D, Abbas M, Ginart A, Ali I, Kalgren P, Vachtsevanos G (2010) Turn-off time as a precursor for gate bipolar transistor latch-up faults in electric motor drives. In: Annual conference of the Prognostics and Health Management Society, Portland, OR

    Google Scholar 

  14. Brown D, Edwards D, Georgoulas G, Zhang B, Vachtsevanos G (2008) Real-time fault detection and accommodation for COTS resolver position sensors. 1st international conference on Prognostics and Health Management (PHM), 6–9 Oct 2008

    Google Scholar 

  15. Straub D (2004) Generic approaches to risk based inspection planning for steel structures. Institute of Structural Engineering, Swiss Federal Institute of Technology, Zürich

    Google Scholar 

  16. Orchard M, Vachtsevanos G (2009) A particle filtering approach for on-line fault diagnosis and failure prognosis. Trans Inst Measurement Control 31(3–4):221–246

    Article  Google Scholar 

  17. Orchard M, Vachtsevanos G, Goebel K (2011) Machine learning and knowledge discovery for engineering systems health management. In: Han J (ed) A combined model-based and data-driven prognostic approach for aircraft system life management. Chapman and Hall/CRC, Boca Raton, pp 363–394

    Google Scholar 

  18. Orchard M, Tang L, Goebel K, Vachtsevanos G (2009) A novel RSPF approach to prediction of high-risk, low-probability failure events. In: First annual conference of the Prognostics and Health Management Society—PHM09, San Diego, CA, USA

    Google Scholar 

  19. Engel S, Gilmartin B, Bongort K, Hess A (2000) Prognostics, the real issues involved with predicting life remaining. In: IEEE Aerospace. Big Sky, MT, pp 457–469

    Google Scholar 

  20. Lewis F (1986) Optimal estimation: with an introduction to stochastic control theory

    Google Scholar 

  21. Jardim-Gonçalves R, Martins-Barata M, Assis-Lopes J, Steiger-Garcao A (1996) Application of stochastic modelling to support predictive maintenance for industrial environments. In: IEEE international conference on systems, man, and cybernetics, pp 117–122

    Google Scholar 

  22. Groer P (2000) Analysis of time to failure with a Weibull mode. In: Maintenance and reliability conference, MARCON 2000

    Google Scholar 

  23. Frelicot C (1996) A fuzzy-based prognostic adaptive system. RAIRO-APII-JESA. J Eur Syst Autom 30(2–3):281–299

    Google Scholar 

  24. Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice-Hall, New Jersey

    Google Scholar 

  25. Aha D (1997) Special issue on lazy learning. Artif Intell Rev 11(1–5):1–6

    Google Scholar 

  26. Studer L, Masulli F (1996) On the structure of a neuro-fuzzy system to forecast chaotic time series. In: International symposium on neuro-fuzzy systems, pp 103–110

    Google Scholar 

  27. Schwabacher M, Goebel K (2007) A survey of artificial intelligence for prognostics. NASA Ames Research Center

    Google Scholar 

  28. Li Y, Kurfess TR, Liang SY (2000) Stochastic prognostics for rolling element bearings. Mech Syst Signal Process 14:747–762

    Article  Google Scholar 

  29. Muench D, Kacprzynski G, Liberson A, Sarlashkar A, Roemer M (2004) Model and sensor fusion for prognosis, example: Kalman filtering as applied to corrosion-fatigue and FE models. SIPS quarterly review presentation

    Google Scholar 

  30. Johnson S et al (2011) Prognostics. In: System health management with aerospace applications. Wiley, Chichester, Ch. 17, Sec. 1, pp 282–283

    Google Scholar 

  31. Pham H, Yang B (2010) Estimation and forecasting of machine health condition using ARMA/GARCH model. In: Mechanical systems and signal processing, pp 546–558

    Article  Google Scholar 

  32. Tangirala R (1996) A nonlinear stochastic model of fatigue crack length for on-line damage sensing. In: Decision and control conference

    Google Scholar 

  33. Roemer M et al (2006) An overview of selected prognostic technologies with application to engine health management. In: Proceedings of GT2006 ASME Turbo Expo, Barcelona, Spain

    Google Scholar 

  34. Arulampalam S, Maskel S, Gordon N, Clapp T (2002) A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  35. Orchard M, Tang L, Saha B, Goebel K, Vachtsevanos G (2010) Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices. Stud Inf Control 19(3):209–218

    Google Scholar 

  36. Byington CS, Roemer MJ (2009) Selected prognostic methods with applications to integrated health management system. In: Applications of intelligent control to engineering systems, Springer, New York, Ch. 1, Sec. 1.6, pp 10–11

    Chapter  Google Scholar 

  37. Schömig A, Rose O (2003) On the suitability of the Weibull distribution for the approximation of machine failures. In: Proceedings of the 2003 industrial engineering research conference, Portland, OR

    Google Scholar 

  38. Edwards D, Orchard M, Tang L, Goebel K, Vachtsevanos G (2010) Impact of input uncertainty on failure prognostic algorithms: extending the remaining useful life of nonlinear systems. In: Prognostics and health management conference

    Google Scholar 

  39. Saxena A, Celaya J, Balaban E, Goebel K, Saha B, Saha S, Schwabacher M (2008) Metrics for evaluating performance of prognostic techniques. In: Proceedings of international conference on Prognostics and Health Management

    Google Scholar 

  40. Saxena A, Celaya J, Saha B, Saha S, Goebel K (2009) On applying the prognostic performance metrics. In: Annual conference of the Prognostics and Health Management Society (PHM09), San Diego, CA

    Google Scholar 

  41. Craig HL, Sprowls DO, Piper DE (1971) In: Ailor WH (ed) Stress corrosion cracking, handbook on corrosion testing and evaluation. Wiley, New York, pp 231–290

    Google Scholar 

  42. Katwan MJ, Hodgkiess T, Arthur PD (1996) Electrochemical noise technique for the prediction of corrosion rate of steel in concrete materials and structures. Mater Struct 29(5):286–294

    Article  CAS  Google Scholar 

  43. Brooks C, Peeler D, Honeycutt KT, Prost-Domasky S (1998) Predictive modeling for corrosion management: modeling fundamentals. In: Predictive modeling for corrosion management: modeling fundamentals, Corfu Greece

    Google Scholar 

  44. Kumar S, Vichare N, Dolev E, Pecht M (2012) A health indicator method for degradation detection of electronic products. Microelectron Reliab 52(2):439–445

    Article  Google Scholar 

  45. Lee S, Younsi K, Kliman G (2005) An online technique for monitoring the insulation condition of ac machine stator windings energy conversion. IEEE Trans Energy Convers 20(4):737–745

    Article  Google Scholar 

  46. Zhang B, Sconyers C, Byington C, Patrick R, Orchard M, Vachtsevanos G (2008) Anomaly detection: a robust approach to detection of unanticipated faults. In: IEEE conference on prognostics and health management, Denver, CO

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Georgia Tech, Atlanta, USA

    George Vachtsevanos

Authors
  1. George Vachtsevanos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Vachtsevanos .

Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    George Vachtsevanos

  2. Department of Materials Engineering, Indian Institute of Science, Bangalore, Karnataka, India

    K. A. Natarajan

  3. drR2 Consulting, LLC, West Hartford, CT, USA

    Ravi Rajamani

  4. Department of Mechanical Engineering, University of Maryland, College Park, MD, USA

    Peter Sandborn

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vachtsevanos, G. (2020). Corrosion Diagnostic and Prognostic Technologies. In: Vachtsevanos, G., Natarajan, K., Rajamani, R., Sandborn, P. (eds) Corrosion Processes. Structural Integrity, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-32831-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32831-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32830-6

  • Online ISBN: 978-3-030-32831-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation