During the condition monitoring of a planetary gearbox, features are extracted from raw data for a fault diagnosis. However, diferent features have diferent sensitivity for identifying diferent fault types, and thus, the selection of a sensitive feature subset from an entire feature set and retaining as much of the class discriminatory information as possible has a directly efect on the accuracy of the classifcation results. In this paper, an improved hybrid feature selection technique (IHFST) that combines a distance evaluation technique (DET), Pearson's correlation analysis, and an ad hoc technique is proposed. In IHFST, a temporary feature subset without irrelevant features is frst selected according to the distance evaluation criterion of DET, and the Pearson's correlation analysis and ad hoc technique are then employed to fnd and remove redundant features in the temporary feature subset, respectively, and hence, a sensitive feature subset without irrelevant or redundant features is selected from the entire feature set. Further, the k-means clustering method is applied to classify the diferent kinds of health conditions. The efectiveness of the proposed method was validated through several experiments carried out on a planetary gearbox with incipient cracks seeded in the tooth root of the sun gear, planet gear, and ring gear. The results show that the proposed method can successfully distinguish the diferent health conditions of a planetary gearbox, and achieves a better classifcation performance than other methods. This study proposes a sensitive feature subset selection method that achieves an obvious improvement in terms of the accuracy of the fault classifcation.
During the condition monitoring of a planetary gearbox, features are extracted from raw data for a fault diagnosis. However, diferent features have diferent sensitivity for identifying diferent fault types, and thus, the selection of a sensitive feature subset from an entire feature set and retaining as much of the class discriminatory information as possible has a directly efect on the accuracy of the classifcation results. In this paper, an improved hybrid feature selection technique (IHFST) that combines a distance evaluation technique (DET), Pearson's correlation analysis, and an ad hoc technique is proposed. In IHFST, a temporary feature subset without irrelevant features is frst selected according to the distance evaluation criterion of DET, and the Pearson's correlation analysis and ad hoc technique are then employed to fnd and remove redundant features in the temporary feature subset, respectively, and hence, a sensitive feature subset without irrelevant or redundant features is selected from the entire feature set. Further, the k-means clustering method is applied to classify the diferent kinds of health conditions. The efectiveness of the proposed method was validated through several experiments carried out on a planetary gearbox with incipient cracks seeded in the tooth root of the sun gear, planet gear, and ring gear. The results show that the proposed method can successfully distinguish the diferent health conditions of a planetary gearbox, and achieves a better classifcation performance than other methods. This study proposes a sensitive feature subset selection method that achieves an obvious improvement in terms of the accuracy of the fault classifcation.
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