文章基本信息

标题：Fault detection and estimation using kernel principal component analysis
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作者：Maya Kallas ; Gilles Mourot ; Kwami Anani 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：1025-1030
DOI：10.1016/j.ifacol.2017.08.212
语种：English
出版社：Elsevier
摘要：AbstractThe principal component analysis (PCA) is a linear technique widely used to retrieve a subspace that maximizes the variance of the data, making the presence of a fault easy to detect. Nevertheless, the real systems are nonlinear. To this end, we propose in this paper to use a kernel-based technique known as kernel principal component analysis (KPCA) for fault diagnosis. The main idea is to use a nonlinear transformation that projects data into a higher dimensional feature space, where conventional PCA is applied. Although detection can be defined in this space, the estimation of the fault requires the map back to the input space. In this sense, we derive an iterative pre-image technique. A study on possible initial points is done. Three initialization techniques based on different properties are presented. The relevance of the proposed technique is illustrated on simulated data.
关键词：KeywordsFault diagnosisnonlinear systemskernel principal component analysisoptimization problem