A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery

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• 作者：Xiaoming Xue^a ; ^{xue_xiaoming@foxmail.com} ; Jianzhong Zhou^b
• 关键词：Rolling element bearing ; Fault diagnosis ; Statistical analysis ; Artificial intelligence ; Fast EEMD ; Modified LGPCA
• 刊名：ISA Transactions
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：66
• 期：Complete
• 页码：284-295
• 全文大小：1108 K
• 卷排序：66

文摘

A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery is proposed. The proposed method is divided into three steps. A preliminary judgment can be evaluated by the statistical analysis method based on the permutation entropy. A novel manifold learning method, modified LGPCA, is introduced to realize the low-dimensional representations for high-dimensional dataset. The results demonstrate the effectiveness of the proposed method.