2018
DOI: 10.3233/jifs-169526
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Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN

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Cited by 84 publications
(41 citation statements)
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“…Previous studies with traditional features in time domain and frequency domain such as power spectral density (PSD) or kurtosis were done obtaining good results in severe faults and laboratory conditions, 33 but, if incipient faults have been studied, the amplitude of the spectra is too low to discriminate between signal and noise.…”
Section: Vibration Processing Methodologymentioning
confidence: 99%
“…Previous studies with traditional features in time domain and frequency domain such as power spectral density (PSD) or kurtosis were done obtaining good results in severe faults and laboratory conditions, 33 but, if incipient faults have been studied, the amplitude of the spectra is too low to discriminate between signal and noise.…”
Section: Vibration Processing Methodologymentioning
confidence: 99%
“…Statistical feature dataset is small and simple to extract, and it allows a highly accurate classification of the set of 13 faulty conditions under study. This set of features has been selected and applied to fault classification in gearboxes and roller bearings [39] providing high classification accuracy. The set of features based on SD is also simple to calculate and provides a high classification accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…A set of classical statistical features was considered for comparing to the performance of the proposed CCM and SD features. The set of statistical features has been studied in [39] for detection of multi-fault in roller bearings and gearboxes. The features are the mean value, root mean square value, standard deviation, kurtosis, maximum value, crest factor, rectified mean value, shape factor, impulse factor, variance, minimum value and skewness.…”
Section: Statistical Featuresmentioning
confidence: 99%
“…erefore, in order to achieve a comprehensive fault feature extraction of rolling bearing, a new fault feature extraction method named hierarchical dispersion entropy is proposed. e fault features of vibration signals of rolling bearing are extracted by the hierarchical dispersion entropy and inputted into the KNN classifier [17] to realize fault diagnosis.…”
Section: Introductionmentioning
confidence: 99%