文章基本信息

标题：A Data-Driven Clustering Algorithm for Residual Data Using Fault Signatures and Expectation Maximization
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作者：Kevin Lindström ; Max Johansson ; Daniel Jung 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：6
页码：121-126
DOI：10.1016/j.ifacol.2022.07.116
语种：English
出版社：Elsevier
摘要：AbstractClustering is an important tool in data-driven fault diagnosis to make use of unlabeled data. Collecting representative data for fault diagnosis is a difficult task since faults are rare events. In addition, using data collected from the field, e.g., logged operational data and data from different workshops about replaced components, can result in labelling uncertainties. A common approach for fault diagnosis of dynamic systems is to use residual-based features that filter out system dynamics while being sensitive to faults. The use of conventional clustering algorithms is complicated by that the distribution of residual data from one fault class varies for different realizations and system operating conditions. In this work, a clustering algorithm is proposed for residual data that clusters data by estimating fault signatures in residual space. The proposed clustering algorithm can be used on time-series data by clustering batches of data from the same fault scenario instead of clustering data sample-by-sample. The usefulness of the proposed clustering algorithm is illustrated using residual data from different fault scenarios collected from an internal combustion engine test bench.
关键词：KeywordsUnsupervised learningData clusteringFault diagnosisMachine learning