文献类型：期刊文献

英文题名：Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS

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

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang, Peoples R China;[2]Mie Univ, Grad Sch Bioresources, Tsu, Mie, Japan

年份：2021

卷号：2021

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000668997700002)、、EI(收录号：20212410494493)、Scopus(收录号：2-s2.0-85107577592)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grant no. 52071090. The authors are thankful to Dr. Yao Lizhong, School of Electrical Engineering, Chongqing University of Science and Technology, for his generous support and suggestions to the work.

语种：英文

外文关键词：Feature extraction - Extraction

外文摘要：Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.