The paper presents a novel approach for prognostics of faults in mechanical drives under non-stationary operating conditions. The feature time series is modeled as an output of a dynamical state-space model, where operating conditions are treated as known model inputs. An algorithm for on-line model estimation is adopted to find the optimal model at the current state of failure. This model is then used to determine the presence of the fault and predict the future behavior and remaining useful life of the system. The approach is validated using the experimental data on a single stage gearbox.
How to Cite
model-based prognostics, dynamic linear model, expectation-maximization algorithm, non-stationary operating conditions
DeCastro, J. A., Liang, T., Kenneth, L. A., Goebel, K., & Vachtsevanos, G. (2009). Exact nonlinear Filtering and Prediction in Process Model-Based Prognostics. In Proceedings of the 1st Annual conference of the PHM Society, San Diego, USA, September 27 - October 1, 2009.
Edwards, D., Orchard, M. E., Tiang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems. In Annual Conference of the Prognostics and Health Management Society, 2010.
Gašperin, M., Juričić , D., Boškoski, P., & Vižintin , J. (2011). Model-based prognostics of gear health using stochastic dynamical models. Mechanical Systems and Signal Processing, 25(2), 537-548.
Gibson, S., & Ninness, B. (2005). Robust Maximum- Likelihood Estimation of Multivariable Dynamic Systems. Automatica, 41, 1667-1682.
Haykin, S. (Ed.). (2001). Kalman Filtering and Neural Networks. John Wiley & Sons, New York, USA.
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, 724-739.
Howard, I., Jia, S., & Wang, J. (2001). The dynamic modeling of a spur gear in mesh including friction and crack. Mechanical Systems and Signal Processing, 15, 831-853.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008). Advances in uncertainty representation and management for particle filtering applied to prognostics. In International Conference on Prognostics and Health Management, 6-9 Oct. 2008, Denver, CO.
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particle- filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31, 221-246.
Randall, R. (1982). A New Method of Modeling Gear Faults. Journal of Mechanical Design, 104, 259-267.
Zhang, B., Khawaja, T., Patrick, R., Vachtsevanos, G., Orchard, M., & Saxena, A. (2009). A Novel Blind Deconvolution De-Noising Scheme in Failure Prognosis. In K. P. Valavanis (Ed.), Applications of Intelligent Control to Engineering Systems (Vol. 39, p. 37- 63). Springer Netherlands.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.