Fault Detection in Non Gaussian Problems Using Statistical Analysis and Variable Selection



João P. P. Gomes Bruno P. Leão Roberto K. H. Galvão Takashi Yoneyama


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

P. P. Gomes, J. ., P. Leão, B. ., K. H. Galvão, R. ., & Yoneyama, T. . (2011). Fault Detection in Non Gaussian Problems Using Statistical Analysis and Variable Selection. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.1977
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fault detection, multivariate statistical analysis, APU

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