文献类型：期刊文献

英文题名：Bearing Fault Diagnosis Using Reconstruction Adaptive Determinate Stationary Subspace Filtering and Enhanced Third-Order Spectrum

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

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524088, Peoples R China;[2]Mie Univ, Grad Sch Bioresources, Tsu, Mie 5148507, Japan;[3]Huangshan Univ, Sch Mech Elect & Informat Engn, Huangshan 245041, Peoples R China

年份：2022

卷号：22

期号：11

起止页码：10764

外文期刊名：IEEE SENSORS JOURNAL

收录：SCI-EXPANDED(收录号：WOS:000804789800077)、、EI(收录号：20221712038367)、Scopus(收录号：2-s2.0-85128638104)、WOS

基金：This work was supported in part by the Program for Scientific Research Start-Up Funds of Guangdong Ocean University and in part by the Key Research and Development Project from Anhui Province of China under Grant 202004a05020025 and Grant 202104b11020011. The associate editor coordinating the review of this article and approving it for publication was Dr. Dong Wang.

语种：英文

外文关键词：Filtering; Vibrations; Fault diagnosis; Trajectory; Feature extraction; Matrix decomposition; Frequency-domain analysis; Bearing fault diagnosis; fault feature enhancement; reconstruction adaptive determinate stationary subspace filtering (Rad-SSF); 1; 5-dimensional third-order energy spectrum

外文摘要：Raw vibration signals poorly perform in industrial bearing fault diagnosis because impulse features are damped and masked by disturbances and noises. Fault diagnosis is more challenging due to weak features. This work presents a signal filtering and fault characteristic enhancement method based on reconstruction adaptive determinate stationary subspace filtering (Rad-SSF) and enhanced third-order spectrum to address the above problems. In particular, Rad-SSF reconstructs an adaptive self-determined, decomposed vibration signal trajectory matrix to obtain non-stationary signals. Then, the filtered signal with the best fault characteristics is extracted according to kurtosis. A 1.5-dimensional third-order energy spectrum is performed to enhance the fault characteristics by strengthening the fundamental frequency and eliminating non-coupling harmonics. Finally, the dominant frequency in the spectrum is contrasted to recognize fault diagnosis, referring to theoretical fault characteristic frequency. The feasibility and effectiveness of the proposed method are demonstrated by simulation and engineering signals under different conditions.