Abstract: In order to improve the accuracy of defect recognition, image clarity and detail integrity, a fault recognition method of substation equipment based on time series algorithm was proposed. The framework of infrared remote viewing and fault identification of substation equipment was designed. The hybrid detector PTZ combination was used to collect its infrared and visible video images, transmit the video images to the monitoring center through optical communication equipment and optical fiber. The suspicious fault images were screened and marked through sliding window combined with time series, and the noise was removed by the median denoising method of wavelet threshold transform. The improved watershed algorithm was used to complete the image target segmentation, and the Zernike invariant moment was used to extract its eigenvalue, which was used as the input of support vector machine to complete the substation equipment classification. Combined with the temperature information of the equipment, the defect identification of different equipment was realized by dividing the defect types and designing the defect identification rules of substation equipment. The experimental results showed that after denoising, the image clarity and contrast were greatly improved, the detail integrity of the target equipment was high, and different equipment faults could be identified.