Aiming at the problem of defect test and parameter assignment in the batch diagnosis of power equipment infrared image,PSO and Niblack algorithm are used to separate the equipment thermal image from the background and extract the lowest,highest and average temperature.Then,the SVM sample feature space can be constructed by calculating the temperature rise characteristics of the equipment.The support vector machine(SVM) parameters are optimized by using the optimized bat algorithm(BA),and the equipment defects diagnosis is realized by SVM under the optimal parameter configuration.According to the 220 groups of image sample testing results,the proposed method has high efficiency and accuracy in thermal defects detection of power equipment,and is suitable for batch analysis and processing of unstructured infrared images in large power data.