Fault diagnosis of rotating machinery based on time-frequency image feature extraction

被引：5
|
作者：
Zhang, Shiyi [1 ]
Zhang, Laigang [2 ]
Zhao, Teng [1 ]
Mahmoud Mohamed Selim [3 ]
机构：
[1] Chongqing Jiaotong Univ, Sch Shipping & Naval Architecture, Chongqing, Peoples R China
[2] Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China
[3] Prince Sattam Bin Abdulaziz Univ, Coll Sci & Humanities Alaflaj, Dept Math, Alaflaj, Saudi Arabia
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2020年 / 39卷 / 04期
关键词：
Time-frequency image; rotating machinery; fault diagnosis;
D O I：
10.3233/JIFS-189004
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Aiming at the characteristics of time-frequency analysis of unsteady vibration signals, this paper proposes a method based on time-frequency image feature extraction, which combines non-downsampling contour wave transform and local binary mode LBP (Local Binary Pattern) to extract the features of time-frequency image faults. SVM is used for classification and recognition. Finally, the method is verified by simulation data. The results show that the classification accuracy of the method reaches 98.33%, and the extracted texture features are relatively stable. Also, the method is compared with the other 3 feature extraction methods. The results also show that the classification effect of the method is better than that of the traditional feature extraction method.
引用
收藏
页码：5193 / 5200
页数：8
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