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

2020 ◽  
Vol 39 (4) ◽  
pp. 5193-5200
Author(s):  
Shiyi Zhang ◽  
Laigang Zhang ◽  
Teng Zhao ◽  
Mahmoud Mohamed Selim
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Extraction Methods ◽  
Image Feature ◽  
Simulation Data ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Better Than

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.

New Feature Extraction Method Based on Contourlet Transform for Banknote Classification

2013 ◽  
Vol 321-324 ◽  
pp. 1061-1065
Author(s):  
Guo Wei Yang ◽  
Wen Ling Wang ◽  
Shan Gai
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Extraction Methods ◽  
Image Feature ◽  
Contourlet Transform ◽  
Image Texture ◽  
Statistical Characteristics ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
New Feature

In order to improve the performance of the banknote classification, new banknote image feature extraction method is proposed in this paper. The contourlet transform is applied to the original banknote image which is obtained by image contact sensor.The statistical characteristics of transformed image in the contourlet domain are analyzed. The statistical characteristics which can perfectly reflect the banknote image texture information are used as feature vector for banknote classification. The experimental results show that the proposed method can obtain higher recognition compared with other conventional banknote image feature extraction methods.

A modified welding image feature extraction algorithm for rotating arc narrow gap MAG welding

2015 ◽  
Vol 42 (3) ◽  
pp. 222-227 ◽  
Author(s):  
Wenhang Li ◽  
Yunhong Ji ◽  
Jing Wu ◽  
Jiayou Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Image Feature ◽  
Mag Welding ◽  
Narrow Gap ◽  
Image Feature Extraction ◽  
Content Type ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
Welding Image

Purpose The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process. Design/methodology/approach An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method. Findings The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection. Practical implications The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding. Originality/value The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.

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