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Copyright of Chinese Journal of Andrology is the property of Chinese Journal of Andrology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenyang Pharmaceutical University is the property of Shenyang Pharmaceutical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the difficulty of data feature selection under different working conditions and the low recognition rate of single classifier in rolling bearing fault diagnosis, this paper proposed a rolling bearing fault diagnosis algorithm based on discrete wavelet transform and random forest. Firstly the proposed method decomposed the vibration signal by discrete wavelet transform to get n-layer approximate coefficients. Then,it used the sigmoid entropy to construct n-dimensional eigenvectors innovatively. The sigmoid entropy could extract the features of non-stationary signals better and improve the diagnostic accuracy. Finally this paper used random forest to diagnose different fault signals of rolling bearing. It used the bearing data provided by the bearing data center website of Case Western Reserve University for experiments. Comparing with the results of traditional classifier(KNN and SVM) and single classification regression tree CART, this method has better diagnostic results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Petroleum Machinery is the property of China Petroleum Machinery Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)