A Two-Stage Model of State Prognosis Based on Vibration Monitoring Information

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The paper puts forward a two-stage model of state prognosis based upon measured vibration monitoring information to date. First the two-stage operating process of equipment is described. Secondly, the modelling method and process of the two-stage state prognosis model is given. At the same time, the estimation method of unknown parameters in model is also given. Last the model is applied to predict the residual life of a set of rolling element bearings based on their vibration monitoring information.

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2182-2188

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August 2010

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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[1] P.J. Volk, J.L. Coetzee, D. Banjevic, A.K.S. Jardine and V. Makis. Optimal Component Replacement Decisions Using Vibration Monitoring and the Proportional-Hazards Model. Journal of the Operational Research Society. Vol. 53 (2002), pp.193-202.

DOI: 10.1057/palgrave.jors.2601261

Google Scholar

[2] D. Lin, M. Wiseman, D. Banjevic and A.K.S. Jardine. An Approach to Signal Processing and Condition-Based Maintenance for Gearboxes Subject to Tooth Failure. Mechanical System and Signal Processing. Vol. 18 (2004), pp.993-1007.

DOI: 10.1016/j.ymssp.2003.10.005

Google Scholar

[3] Baruah, P. and Chinnam, R.B. HMMs for Diagnostics and Prognostics in Machining Processes, International Journal of Production Research. Vol. 43 (2005), pp.1275-1293.

DOI: 10.1080/00207540412331327727

Google Scholar

[4] Ming Dong and David He. Hidden Semi-markov Model- based Methodology for Multi-sensor Equipment Health Diagnosis and Prognosis. European Journal of Operational Research. Vol. 178 (2007), pp.858-878.

DOI: 10.1016/j.ejor.2006.01.041

Google Scholar

[5] R.B. Chinnam, P. Baruah. A Neuro-Fuzzy Approach for Estimating Mean Residual Life in Condition-Based Maintenance Systems. International Journal of Materials and Product Technology. Vol. 20(2004), pp.166-179.

DOI: 10.1504/ijmpt.2004.003920

Google Scholar

[6] Sze-jung Wu, Nagi Gebraeel, Mark A. Lawley. A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy. IEEE Transactions on systems, man, and cybernetics-paper A: Systems and Humans, Vol. 37 (2007).

DOI: 10.1109/tsmca.2006.886368

Google Scholar

[7] G. J. Kacprzynski, A. Sarlashkar and M.J. Roemer. Predicting Remaining Life By Fusing the Physics of Failure Modeling with Diagnostics. Journal of Metal. (2004), p.56: 29-35.

DOI: 10.1007/s11837-004-0029-2

Google Scholar

[8] W. Matthew, B. Carl, E. Douglas Edwards and A. Sanket. Dynamic Modeling and Wear-Based Remaining Useful Life Prediction of High Power Clutch Systems. Tribology Transactions. Vol. 48 (2005), pp.208-217.

DOI: 10.1080/05698190590927451

Google Scholar

[9] Wang and Christer. Towards a General Condition-Based Maintenance Model for a Stochastic Dynamic System, Journal of Operational Research Society. Vol. 51 (2000), pp.145-155.

DOI: 10.1057/palgrave.jors.2600863

Google Scholar

[10] W. Wang. A Model to Predict the Residual Life of Rolling Element Bearings Given Monitored Condition Information to Date. IMA J. Management Mathematics. Vol. 13 (2002), pp.3-16.

DOI: 10.1093/imaman/13.1.3

Google Scholar