Automatic Defect Detection Method for the Steel Cord Conveyor Belt Based on Its X-Ray Images

2011 ◽  
Author(s):  
Xian-Guo Li ◽  
Chang-Yun Miao ◽  
Ji Wang ◽  
Yan Zhang
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Conveyor Belt ◽  
X Ray ◽  
Steel Cord

Fault Automatic Detection Method of Steel Cord Conveyor Belt Based on Gabor Filter Bank

2013 ◽  
Vol 411-414 ◽  
pp. 1218-1221
Author(s):  
Yan Zhang ◽  
Wei Gong ◽  
Wei Liang Zhong
Keyword(s):  
Filter Bank ◽  
Detection Method ◽  
Gabor Filter ◽  
Automatic Detection ◽  
Conveyor Belt ◽  
Gabor Filters ◽  
Fusion Images ◽  
Steel Cord ◽  
Gabor Filter Bank ◽  
Good Detection

A method based on Gabor filter bank is presented to automatically detect the steel cord conveyor belt fault. Firstly, multi-channel filtering is implemented by Gabor filters. Then the characteristic differential and fusion images are calculated. Finally, the fault images are identified by white pixels statistics after the threshold segmentation. The results show that this method has good detection result for steel cord conveyor belt fault.

Automatic Detection Apparatus Development of Steel Cord Conveyor Belt

2010 ◽  
Vol 455 ◽  
pp. 516-520
Author(s):  
Y.N. Yun ◽  
H.M. Zhang ◽  
C.J. Li
Keyword(s):  
Large Scale ◽  
Large Angle ◽  
Automatic Detection ◽  
Conveyor Belt ◽  
Industrial Applications ◽  
Transportation Systems ◽  
Long Distance ◽  
X Ray ◽  
Steel Core ◽  
Steel Cord

Steel core belts are widely used in long-distance, large capacity, large angle of mine, metallurgy, port, electricity and other departments of the large-scale transportation systems. According to incomplete statistics, this kind of belt accident 93.75% occurred in the joints and most are due to joints tic was not timely detected. Hence, non-destructive detection of belt is the key to the whole transportation system’s safety. For steel core belt automatic detection technology, deep research has been carried out at home and abroad. Detection apparatus was developed based on the electromagnetic theory and principle of X-ray, however, some varying defects existed in the industrial applications. Automatic detection apparatus of steel cord conveyor belt, developed by Pingdingshan Industry Polytechnic College and Shanxi Huaning Beier Measurement & Control Co. Ltd., integrated both advantages of electromagnetic detection and X-ray detection. It overcomes the difficulties of X-ray machine explosion proof and long time radiation. This kind of automatic detection apparatus has been installed and worked well in more than 30 mines and achieved good economic and social benefits since it was installed in Zhong Ping Energy Chemical Group’s 12th main slope conveyor.

scholarly journals Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qiang Guo ◽  
Caiming Zhang ◽  
Hui Liu ◽  
Xiaofeng Zhang
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Weighted Averaging ◽  
X Ray ◽  
Feature Vectors ◽  
Detection Algorithms ◽  
Quantitative Metrics ◽  
Feature Similarity ◽  
Anomaly Map

Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

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