An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

Fault detection has become extremely important in industrial production so that numerous potential losses caused from equipment failures could be saved. As a noncontact method, machine vision can satisfy the needs of real-time fault monitoring. However, image-based fault features often have the characteristics of high-dimensionality and redundant correlation. To optimize feature subsets and SVM parameters, this paper presents an enhanced artificial bee colony-based support vector machine (EABC-SVM) approach. The method is applied to the image-based fault detection for the conveyor belt. To improve the optimized capability of original ABC, the EABC algorithm introduces two enhanced strategies including the Cat chaotic mapping initialization and current optimum based search equations. Several UCI datasets have been used to evaluate the performance of EABC-SVM and the experimental results show that this approach has better classification accuracy and convergence performance than the ABC-SVM and other ABC variants-based SVM. Furthermore, the EABC-SVM can achieve a significant detection accuracy of 95% and reduce the amount of features about 65% in the conveyor belt fault detection.