2007
DOI: 10.1016/j.ymssp.2006.06.010
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Decision tree and PCA-based fault diagnosis of rotating machinery

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Cited by 226 publications
(117 citation statements)
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“…In the second phase, because the tree can overfit the training data, the overfitted branches of the tree are removed. More information related to the C4.5 algorithm can be obtained from He et al [29], Uguz [30], and Sun et al [31].…”
Section: C45 Algorithmmentioning
confidence: 99%
“…In the second phase, because the tree can overfit the training data, the overfitted branches of the tree are removed. More information related to the C4.5 algorithm can be obtained from He et al [29], Uguz [30], and Sun et al [31].…”
Section: C45 Algorithmmentioning
confidence: 99%
“…In general, time-frequency techniques are effective for non-stationary signals. Furthermore, the classification schemes, such as artificial neural networks [25,26], decision tree [27,28], and support vector machine (SVM) [27,29,30], have also been introduced for identifying different types of bearing faults.…”
Section: Introductionmentioning
confidence: 99%
“…In general, the classification schemes, such as artificial neural-fuzzy network [22,25], support vector machine (SVM) [8,12,26] and decision tree [13,30], are utilized to effectively identify the different types of machine faults. In order to increase the computational efficiency of the classification procedure and enhance the accuracy of the diagnosis results, the principal component analysis (PCA) method is to select the certain features of high priority [30,31].…”
Section: Introductionmentioning
confidence: 99%
“…In order to increase the computational efficiency of the classification procedure and enhance the accuracy of the diagnosis results, the principal component analysis (PCA) method is to select the certain features of high priority [30,31]. However, the selected features will lose the original physical representations through the feature space transformation process of PCA.…”
Section: Introductionmentioning
confidence: 99%