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In the nickel electrolysis industry, detection of the conductive state of copper rods is an important part of production procedure management, as abnormal conductive states of the copper rod result in a decline in the quality of the electrodeposited nickel plate. Conventional treatment consists of manual detection and handing, which is inefficient and induces more problems, such as the safety of the insulation. Because abnormal conductive states are only located between the copper rod and busbar, it has obvious position characteristics, and abnormal conductive states also induce a calorific difference in a particular area, which can be detected in an infrared image. We can use the infrared feature and position characteristics to identify the abnormal conductive faults. This paper introduces a method and practice for the identification of abnormal conductive faults in a conductive copper rod in the nickel electrolysis procedure using computer vision theory, including infrared image segmentation with position characteristics, infrared feature extraction, and conductive fault identification with SVM (support vector machine). The result shows that the method can divide the conductive states of the copper rod into abnormal heating conditions, normal operating conditions, and open circuit conditions, with a 90% accuracy rate on the obtained samples.