文献类型：期刊文献

英文题名：Bearing Fault Feature Enhancement and Diagnosis Based on Statistical Filtering and 1.5-Dimensional Symmetric Difference Analytic Energy Spectrum

作者：Liao, Zhiqiang[1];Song, Xuewei[2];Jia, Baozhu[1];Chen, Peng[3]

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524088, Peoples R China;[2]Mie Univ, Grad Sch Environm Oriented Informat & Syst Engn, Tsu, Mie 5148507, Japan;[3]Mie Univ, Grad Sch Environm Sci & Technol, Tsu, Mie 5148507, Japan

年份：2021

卷号：21

期号：8

起止页码：9959

外文期刊名：IEEE SENSORS JOURNAL

收录：SCI-EXPANDED(收录号：WOS:000648573500041)、、EI(收录号：20210709924960)、Scopus(收录号：2-s2.0-85100807221)、WOS

基金：Manuscript received December 24, 2020; accepted January 18, 2021. Date of publication February 1, 2021; date of current version March 17, 2021. This work was supported by the National Natural Science Foundation of China under Grant 52071090. The associate editor coordinating the review of this article and approving it for publication was Prof. Ruqiang Yan.

语种：英文

外文关键词：Filtering; Demodulation; Sensors; Fault diagnosis; Band-pass filters; Signal to noise ratio; Noise measurement; Bearing fault diagnosis; statistical filtering; 1; 5-dimensional symmetric difference analytic energy operator (1; 5D-SDAEO); feature enhancement and diagnosis

外文摘要：Bearing fault impulses are easily submerged by background noise, resulting in the inconspicuous fault feature and affecting the accuracy of the fault diagnosis. This paper presents a novel method for bearing the fault feature enhancement and diagnosis based on the statistical filtering and the 1.5-dimensional symmetric difference analytic energy operator (1.5D-SDAEO) to solve the problem. 1). The statistical filtering is used to filter the background noise under the standard distinction index. 2). The 1.5D-SDAEO is used to enhance the signal impulse, suppress the residual noise, and improve the SNR. 3). The dominant frequency in the energy spectrum is compared with the rolling bearing fault characteristic frequency to the fault diagnosis. The feasibility and the superiority of the presented method are verified by the simulation, engineering, and comparison experiments. All results show that the presented method can effectively enhance the fault feature and accurately diagnose the rolling bearing fault.