Affiliations: [a] School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing, China
| [b] School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong, China
| [c] Department of Mathematics, College of Science and Humanities in Alaflaj, Prince Sattam Bin Abdulaziz University, Alaflaj, Saudi Arabia
Correspondence:
[*]
Corresponding author. Laigang Zhang, School of Mechanical and Automotive Engineering, Liaocheng University,
Liaocheng, Shandong, 252059, China. E-mail: [email protected].
Abstract: 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.
