Complex System Fault Detection Using Factor Analysis



Published Oct 10, 2010
Yilu Zhang


In this paper, we propose to use a data modeling technology, Factor Analysis, in the application of complex system fault diagnosis and failure prognosis. Factor Analysis captures the dominant dependency underlying observable measurements of physical systems, and is sensitive to their changes, as demonstrated by the preliminary experimental results on two real-world datasets. Comparison studies show that Factor Analysis has advantages over two related techniques,Principal Component Analysis and K-Means.

How to Cite

Zhang, Y. (2010). Complex System Fault Detection Using Factor Analysis. Annual Conference of the PHM Society, 2(1).
Abstract 138 | PDF Downloads 98



battery management systems, fault detection, factor analysis, principal component analysis, K-means

(Chaijaruwanich et al., 2006) J. Chaijaruwanich, J. Khamphachua, S. Prasitwattanaseree, S. Warit, and P. Palittapongarnpim, “Application of Factor Analysis on Mycobacterium Tuberculosis Transcriptional Responses for Drug Clustering, Drug Target, and Pathway Detections,” in Lecture Notes in Computer Science: Advanced Data Mining and Applications, Vol. 4093, pp. 835-844, 2006.
(Ghahramani and Hinton, 1996) Ghahramani, Z. and G.E. Hinton, “The EM Algorithm for Mixtures of Factor Analyzers.” CRG-TR-96-1, University of Toronto, 1996.
(Jain et al., 1999) A.K. Jain, M.N. Murty, P.J. Flynn, “Data Clustering: A Review,” in ACM Computing Surveys, Vol. 31, No. 2, pp. 264-323, 1999.
(Nokhandan et al., 2009) M.H. Nokhandan, G.A. F. Ghalhary and M. Mousavi-Baygi, “The application of factor analysis and artificial neural networks in predicting spring precipitation by means of climatic parameters of the upper levels of atmosphere,” in Trends in Applied Sciences Research, Vol. 4, Issue 2, pp 85-97, 2009.
(Plett, 2004) G.L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, Part 2, modeling and identification”, in Journal of Power Sources, Vol. 134, pp. 262-276, 2004.
(Safdari et al., 2005) Cyrus Safdari, Nancy J. Scannell, Rubina Ohanian, “A Statistical Approach to Peer- Groupings; The Case of Banks in Armenia,” in The Journal of American Academy of Business, Cambridge, Vol. 6, No. 2, March 2005.
(Schwabacher and Goebel, 2007) M. Schwabacher, K. Goebel, “An survey of artificial intelligence for prognostics,” in Proceedings of AAAI Fall Symposium, Arlington, VA, 2007.
(Zhang et al., 2009) Xiaodong Zhang, Ryan Grube, Kwang-Keun Shin, Mutasim Salman, Robert Conell, “Automotive Battery State-of-Health Monitoring: A Parity Relation Based Approach,” in Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (Safeprocess 2009), Barcelona, Spain, June 30-July 3, 2009.
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