This work concers the problem of fault detection using data-driven methods without the assumption of gaussianity. The main idea is extend the Runger's U2 statistical distance measures to the case where the monitored variables are not gaussian. The proposed extension is based on Gaussian Mixture Models and Parzen windows classifiers to estimate the required conditional probability distributions. The proposed methodology was applied to an APU dynamic model and showed better results when compared to classical fault detection techniques using Multivariate Statistical Process control with Hotelling’s T metrics
How to Cite
fault detection, multivariate statistical analysis, APU
Duda, R. O., Hart, P. E., and Stork, D. G. (2001). Pattern Classification. 2nd ed. New York: Wiley. De Maesschalck, R., Jouan-Rimbaud, D., and Massart, D.L. (2000). The Mahalanobis Distance, Chemometrics and Intelligent Laboratory Systems, 50, 1–18, 2000.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B, 39, 1–38.
Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components, Journal of Educational Psychology, 24, 498–520.
Kourti, T. and MacGregor, J. F. (1995). Process Analysis, Monitoring and Diagnosis, Using Multivariate Projection Methods”, Chemometrics and Intelligent Laboratory Systems 28, 3–21.
Kumar, S., Sotiris, V., and Pecht, M. (2008), Mahalanobis Distance and Projection Pursuit Analysis for Health Assessment of Electronic Systems, in Proceedings IEEE Aerospace Conference, Big Sky, MO.
Leão, B. P., Gomes, J. P. P., Galvão, R. K. H., and Yoneyama, T . (2009). Aircraft Flap and Slat Systems Health Monitoring Using Statistical Process Control Techniques, in Proceedings of IEEE Aerospace Conference, Big Sky, MO.
Mahalanobis, P. C. (1936). On the Generalized Distance in Statistics, Proceedings of the National Institute of Science of India, 12, 49–55.
Mimnagh, M. L., Hardman, W., and Sheaffer, J. (2000), Helicopter Drive System Diagnostics Through Multivariate Statistical Process Control, in Proceedings IEEE Aerospace Conference, Big Sky,MO.
Runger, G. C. (1996). Projections and the U2 Multivariate Control Chart”, Journal of Quality Technology, 28, 313–319.
Webb, A (2002), Statistical Pattern Recognition. 2nded. West Sussex: John Wiley and Sons Ltd.
Yacher, L., and Orchard, M. (2003), Statistical Multivariate Analysis and Dynamics Monitoring for Plant Supervision Improvement, in Proceedings Copper International Conference.
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.