Abstract
The health of a mechanical component deteriorates over time and its service life is randomly distributed and can be modeled by a stochastic deterioration process. For most of the mechanical components, the deterioration process follows a certain physical laws and their mean life to failure can be determined approximately by these laws. However, it is not easy to apply these laws for mechanical component prognostics in current health monitoring applications. In this paper, a stochastic modeling methodology for mechanical component prognostics with condition indicators used in current health monitoring applications is presented. The effectiveness of the methodology is demonstrated with a real shaft fatigue life prediction case study.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bechhoefer, E., Bernhard, A., & He, D. (2008). Use of Paris law for prediction of component remaining life. In Proceedings of the 2008 IEEE aerospace conference, Big Sky, Montana.
Endrenyi J., Anders G. J., Leite da Silva A. M. (1998) Probabilistic evaluation of the effect of maintenance on reliability: An application. IEEE Transactions on Power Systems 13(2): 576–583
He, D., & Bechhoefer, E. (2008). Development and validation of bearing diagnostic and prognostic tools using HUMS condition indicators. In Proceedings of the 2008 IEEE aerospace conference, Big Sky, Montana.
Jirutitijaroen P., Singh C. (2004) The effect of transformer maintenance parameters on reliability and cost: A probabilistic model. Electric Power Systems Research 72(3): 213–224
Lam C. T., Yeh R. H. (1994a) Optimal replacement policies for multistate deterioration systems. Naval Research Logistics 41(3): 303–315
Lam C. T., Yeh R. H. (1994b) Optimal maintenance policies for deterioration systems under various maintenance strategies. IEEE Transactions on Reliability 43(3): 423–430
Paris P. C., Erdogan F. (1963) A Critical analysis of crack propagation laws. Journal of Basic Engineering 85(4): 528–534
Theil, G. (2006). Parameter evaluation for extended Markov models applied to condition- and reliability-centered maintenance planning strategies. In Proceedings of the international conference on Probabilistic Methods Applied to Power Systems (PMAPS), Stockholm, Norway.
Van Winden C., Dekker R. (1998) Rationalisation of building maintenance by Markov decision models: A pilot study. Journal of the Operational Research Society 49(9): 928–935
Welte, T. M., Vatn, J., & Heggest, J. (2006). Markov state model for optimization of maintenance and renewal of hydro power components. In Proceedings of the international conference on Probabilistic Methods Applied to Power Systems (PMAPS), Stockholm, Norway.
Wu S., He D. (2008) Development and validation of drive shaft diagnostics and prognostics using damage dynamic simulation. Journal of Risk and Reliability - Proceedings of the Institution of Mechanical Engineers, Part O 222: 219–233
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
He, D., Li, R. & Bechhoefer, E. Stochastic modeling of damage physics for mechanical component prognostics using condition indicators. J Intell Manuf 23, 221–226 (2012). https://doi.org/10.1007/s10845-009-0348-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-009-0348-9