A State-Space Model for Vibration Based Prognostics



Eric Bechhoefer Steve Clark David He


Operation and maintenance of offshore wind farms will be more difficult and expensive than equivalent onshore wind farms. Accessibility for routine servicing and maintenance will be a concern: there may be times when the offshore wind farm is inaccessible due to sea, wind and visibility conditions. Additionally, maintenance tasks are more expensive than onshore due to distance of the wind farm from shore, site exposure, and the need for specialized lifting equipment to install and change out major components .As a result, the requirement for remote monitoring and condition based maintenance techniques becomes more important to maintain optimum turbine availability levels. The development of a prognostics health management (PHM) capability will allow a strategy that balances risk of running the turbine against lost revenue. Prognostics would give an estimate of the remaining useful life of a component under various loads, thus avoiding component failure. We present a state-space model for predicting the remaining useful life of a component based on vibration signatures. The model dynamics are explained and analysis is performed to evaluate the nature of fault signature distribution, and an indicator of prognostic confidence is proposed. The model is then validated under real world conditions.

How to Cite

Bechhoefer, E. ., Clark, S. ., & He, D. (2010). A State-Space Model for Vibration Based Prognostics. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1863
Abstract 71 | PDF Downloads 6



extended Kalman filter, prognostics, State Space

(Meyer, 2009) R. Meyer. Offshore Wind Energy, http://www.oceanenergycouncil.com/index.php/Offs hore-Wind/Offshore-Wind-Energy.html

(Kuhn et al., 1997) M. Kuhn, L.A. Harland, W.A.A.M. Bierbooms, T.T. Cockerill, M.C. Ferguson, “Integrated Design Methodology for Offshore Wind Energy Conversion Systems” Institute for Wind Energy, 1998.

(Vachtsevanos et al., 2006) G. Vachtsevanos, F. L. Lewis, M. Roemer, A. Hess, and B. Wu. Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1st ed. Hoboken, New Jersey: John Wiley & Sons, Inc, 2006.

(Candy, 2009) J. Candy, Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, John Wiley & Sons, Hoboken.

(CAP753, 2006) Helicopter Vibration Health Monitoring (VHM), www.caa.co.uk/docs/33/cap753.pdf

(Brogan, 1991) W. Brogan, Modern Control Theory, Prentice Hall, Upper Saddle River, NJ, 07458, 1991. (Frost et al. 1999) N. Frost, K. March, and L. Pook, Metal Fatigue, 1999, Dover Publications, Mineola,NY., page 228-244.

(Frost 1959) N. Frost, DSR, NEL Rep No PM 287,1959

(Hartikainen and Sarkka 2008) J. Hartikainen, S.Sarkka, RCMCDABox – Matlab of Rao- Blackwellized Data Association Particle Filters, 2008, http://www.lce.hut.fi/research/mm/rbmcda/

(Bechhoefer 2008) Bechhoefer, E., “A Method for Generalized Prognostics of a Component Using Paris Law” American Helicopter Society 64th Annual Forum, Montreal, CA, April 29 - May 1, 2008.

(Orchard et al. 2007) M. Orchard and G. Vachtsevanos, "A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate", International Journal of Fuzzy Logic and Intelligent Systems, vol. 7, no. 4, pp. 221-227, December 2007.

(Bechhoefer and Bernhard 2007) E. Bechhoefer, A. Bernhard, “A Generalized Process for Optimal Threshold Setting in HUMS” IEEE Aerospace Conference, Big Sky, 2007.
Technical Papers

Most read articles by the same author(s)

1 2 3 > >>