Skip to main content
SpringerLink
Log in

New Technologies for Maintenance

  • Chapter
Book cover Complex System Maintenance Handbook

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

Abstract

For years, maintenance has been treated as a dirty, boring and ad hoc job. It’s seen as critical for maintaining productivity but has yet to be recognized as a key component of revenue generation. The question most often asked is “Why do we need to maintain things regularly?” The answer is “To keep things as reliable as possible.” However, the question that should be asked is “How much change or degradation has occurred since the last round of maintenance?” The answer to this question is “I don’t know.” Today, most machine field services depend on sensor-driven management systems that provide alerts, alarms and indicators. The moment the alarm sounds, it’s already too late to prevent the failure. Therefore, most machine maintenance today is either purely reactive (fixing or replacing equipment after it fails) or blindly proactive (assuming a certain level of performance degradation, with no input from the machinery itself, and servicing equipment on a routine schedule whether service is actually needed or not). Both scenarios are extremely wasteful.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

3.5 References

  • Badia, F.G., Berrade, M.D. and Campos, C.A., (2002) Optimal Inspection and Preventive Maintenance of Units with Revealed and Unrevealed Failures. Reliability Engineering and System Safety 78: 157–163.

    Article  Google Scholar 

  • Barbera, F., Schneider, H. and Kelle, P., (1996) A Condition Based Maintenance Model with Exponential Failures and Fixed Inspection Interval. Journal of the Operational Research Society 47(8): 1037–1045.

    Article  MATH  Google Scholar 

  • Bonissone, G., (1995) Soft computing applications in equipment maintenance and service in: ISIE’ 95, Proceedings of the IEEE International Symposium, 2: 10–14.

    Google Scholar 

  • Brotherton, T., Jahns, G., Jacobs, J. and Wroblewski, D., (2000) Prognosis of faults in gas turbine engines, in: Aerospace Conference Proceedings, (2000) IEEE, 6: 18–25.

    Google Scholar 

  • Bruns, P., (2002) Optimal Maintenance Strategies for Systems with Partial Repair Options and without Assuming Bounded Costs. European Journal of Operational Research 139:146–165.

    Article  MATH  MathSciNet  Google Scholar 

  • Bunday, B.D., (1991) Statistical Methods in Reliability Theory and Practice, Ellis Horwood.

    Google Scholar 

  • Burrus, C., Gopinath, R. and Haitao, G., (1998) Introduction to wavelets and wavelet transforms-a primer. NJ: Prentice Hall.

    Google Scholar 

  • Casoetto, N., Djurdjanovic, D., Mayor, R., Lee, J. and Ni, J., (2003) Multisensor process performance assessment through the use of autoregressive modeling and feature maps. Trans. of SME/NAMRI, 31:483–490.

    Google Scholar 

  • Cavory, G., Dupas, R. and Goncalves, R., (2001) A Genetic Approach to the Scheduling of Preventive Maintenance Tasks on a Single Product Manufacturing Production Line, International Journal of Production Economics, 74: 135–146.

    Article  Google Scholar 

  • Chen, C.T., Chen, Y.W. and Yuan, J., (2003) On a Dynamic Preventive Maintenance Policy for a System under Inspection. Reliability Engineering and System Safety 80: 41–47.

    Article  Google Scholar 

  • Chen, D. and Trivedi, K., (2002) Closed-Form Analytical Results for Condition-Based Maintenance. Reliability Engineering and System Safety 76: 43–51.

    Google Scholar 

  • Cohen, L., (1995) Time-frequency analysis. NJ: Prentice Hall.

    Google Scholar 

  • Cox, D., (1972) Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 34:187–220.

    MATH  Google Scholar 

  • Djurdjanovic, D., Widmalm, S.E., William, W.J., et al., (2000) Computerized classification of temporomandibular joint sounds. IEEE Transactions on Biomedical Engineering 47:977–984.

    Article  Google Scholar 

  • Djurdjanovic, D., Ni, J. and Lee, J., (2002) Time-frequency based sensor fusion in the assessment and monitoring of machine performance degradation. Proceedings of 2002 ASME Int. Mechanical Eng. Congress and Exposition, paper number IMECE2002-32032.

    Google Scholar 

  • Garga, A., McClintic, K.T., Campbell, R.L., et al., (2001) Hybrid reasoning for prognostic learning in CBM systems, in: Aerospace Conference, 10–17 March, 2001, IEEE Proceedings, 6: 2957–2969.

    Article  Google Scholar 

  • Goodenow, T., Hardman, W., Karchnak, M., (2000) Acoustic emissions in broadband vibration as an indicator of bearing stress. Proceedings of IEEE Aerospace Conference, 2000; 6: 95–122.L.D.

    Google Scholar 

  • Hall, L.D. and Llinas, J., (Eds.), (2000) Handbook of Sensor Fusion, CRC Press.

    Google Scholar 

  • Hall, L.D., (1992) Mathematical techniques in Multi-Sensor Data Fusion, Artech House Inc.

    Google Scholar 

  • Hansen, R., Hall, D., Kurtz, S., (1994) New approach to the challenge of machinery prognostics. Proceedings of the International Gas Turbine and Aeroengine Congress and Exposition, American Society of Mechanical Engineers, June 13–16 1994:1–8.

    Google Scholar 

  • IMS, NSF I/UCRC Center for Intelligent Maintenance Systems, www.imscenter.net; 2004.

    Google Scholar 

  • Kemerait, R., (1987) New cepstral approach for prognostic maintenance of cyclic machinery. IEEE SOUTHEASTCON, 1987: 256–262.

    Google Scholar 

  • Kleinbaum, D., (1994) Logistic regression. New York: Springer-Verlag.

    MATH  Google Scholar 

  • Labib, A.W., (2006) Next generation maintenance systems: Towards the design of a selfmaintenance machine. 2006 IEEE International Conference on Industrial Informatics, Integrating Manufacturing and Services Systems, 16–18 August, Singapore

    Google Scholar 

  • Lee, J., (1995) Machine performance monitoring and proactive maintenance in computerintegrated manufacturing: review and perspective. International Journal of Computer Integrated Manufacturing 8:370–380.

    Article  Google Scholar 

  • Lee, J., (1996) Measurement of machine performance degradation using a neural network model. Computers in Industry 30:193–209.

    Article  Google Scholar 

  • Lee, J., Ni, J., (2002) Infotronics agent for tether-free prognostics. Proceeding of AAAI Spring Symposium on Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction. Stanford Univ., Palo Alto, CA, March 25–27.

    Google Scholar 

  • Liang, E., Rodriguez, R., Husseiny, A., (1988) Prognostics/diagnostics of mechanical equipment by neural network, Neural Networks 1(1) 33.

    Google Scholar 

  • Liao, H., Lin, D., Qiu, H., Banjevic, D., Jardine, A., Lee, J., (2005) A predictive tool for remaining useful life estimation of rotating machinery components. ASME International 20th Biennial Conference on Mechanical Vibration and Noise, Long Beach, CA, 24–28 September, 2005.

    Google Scholar 

  • Liu, J., Djurdjanovic, D., Ni, J., Lee, J., (2004) Performance similarity based method for enhanced prediction of manufacturing process performance. Proceedings of the 2004 ASME International Mechanical Engineering Congress and Exposition (IMECE), 2004.

    Google Scholar 

  • Marple, S., (1987) Digital spectral analysis. NJ: Prentice Hall.

    Google Scholar 

  • Marseguerra, M., Zio, E. and Podofillini, L. (2002) Condition-Based Maintenance Optimization by Means of Genetic Algorithm and Monte Carlo Simulation. Reliability Engineering and System Safety 77: 151–166.

    Article  Google Scholar 

  • Mijailovic, V. (2003) Probabilistic Method for Planning of Maintenance Activities of Substation Component. Electric Power System Research 64: 53–58.

    Article  Google Scholar 

  • Pandit, S., Wu, S-M., (1993) Time series and system analysis with application. FL: Krieger Publishing Co.

    Google Scholar 

  • Parker, B.E., Jr., Nigro, T.M., Carley, M.P., et al., (1993) Helicopter gearbox diagnostics and prognostics using vibration signature analysis, in: Proceedings of the SPIE — The International Society for Optical Engineering: 531–542.

    Google Scholar 

  • Pham, H., Suprasad, A. and Misra, R.B. (1997) Availability and Mean Life Time Prediction of Multistage Degraded System with Partial Repairs. Reliability Engineering and System Safety 56: 169–173

    Article  Google Scholar 

  • Radjou, N., (2002) The collaborative product life-cycle. Forrester Research, May 2002.

    Google Scholar 

  • Ray, A. and Tangirala, S., (1996) Stochastic Modeling of Fatigue Crack Dynamic for On-Line Failure Prognostics, IEEE Transactions on Control Systems Technology, 4(4): 443–449.

    Article  Google Scholar 

  • Reichard, K., Van Dyke, M. and Maynard, K. (2000) Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system. Proceedings of SPIE-The International Society for Optical Engineering 4051:329–336.

    Google Scholar 

  • Roemer, M., Kacprzynski, G. and Orsagh, R., (2001) Assessment of data and knowledge fusion strategies for prognostics and health management. Proceedings of IEEE Aerospace Conference, 2001; 6:62979–62988.

    Google Scholar 

  • Seliger, G., Basdere, B., Keil, T., et al. (2002) Innovative processes and tools for disassembly. Annals of CIRP 51:37–41.

    Google Scholar 

  • Su, L., Nolan M, DeMare G, Carey D. (1999) Prognostics framework ‘for weapon systems health monitoring’. Proceedings of IEEE Systems Readiness Technology Conference, IEEE AUTOTESTCON’ 99, 30 August–2 September 1999: 661–672.

    Google Scholar 

  • Swanson, D.C., (2001) A General Prognostics tracking algorithm for predictive maintenance, Proc. of the IEEE Aerospace Conference, 2001, 6: 2971–2977.

    Google Scholar 

  • Tacer, B., Loughlin, P., (1996) Time-frequency based classification. SPIE Proceedings 42:2697–2705.

    Google Scholar 

  • Thurston, M. and Lebold, M., (2001) Open Standards for Condition Based Maintenance and Prognostic Systems, Pennsylvania State University, Applied Research Laboratory.

    Google Scholar 

  • Umeda, Y., Tomiyama, T. and Yoshikawa, H., (1995) A design methodology for selfmaintenance machines, ASME journal of mechanical design, 117, September

    Google Scholar 

  • Vachtsevanos, G. and Wang, P., (2001) Fault prognosis using dynamic wavelet neural networks, Proceedings of the IEEE International Symposium on Intelligent Control 2001 (ISIC’ 01): 79–84.

    Google Scholar 

  • Wang, H.Z., (2002) A Survey of Maintenance Policies of Deteriorating Systems, European Journal of Operationa Research, 139: 469–489.

    Article  MATH  Google Scholar 

  • Wilson, B.W., Hansen, N.H., Shepard, C.L., et al. (1999) Development of a modular in-situ oil analysis prognostic system. International Society of Logistics (SOLE) 1999 Symposium, Nevada, Las Vegas, 30 August–2 September.

    Google Scholar 

  • Yamada, A., Takata, S., (2002) Reliability improvement of industrial robots by optimizing operation plans based on deterioration evaluation. Annals of the CIRP 51:319–322.

    Article  Google Scholar 

  • Yen, G., Lin, K., (2000) Wavelet packet feature extraction for vibration monitoring. IEEE Trans. on Industrial Electronics 2000; 47:650–667.

    Article  Google Scholar 

  • Zalubas, E.J., O’Neill, J.C., Williams, W.J., et al., (1996) Shift and scale invariant detection. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 1996; 5:3637–3640.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Manufacturing, University of Cincinnati, Cincinnati

    Jay Lee (Ohio Eminent Scholar and L.W. Scott Alter Chair Professor)

  2. NSF Industry/University Cooperative Research Centre (I/UCRC) on Intelligent Maintenance Systems (IMS) Center headquartered, University of Cincinnati, Cincinnati

    Haixia Wang (postdoctoral researcher)

Authors
  1. Jay Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haixia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag London Limited

About this chapter

Cite this chapter

Lee, J., Wang, H. (2008). New Technologies for Maintenance. In: Complex System Maintenance Handbook. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-1-84800-011-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-84800-011-7_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-010-0

  • Online ISBN: 978-1-84800-011-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation