Large equipment such as transformers has the characteristics of identification and stability during operation, but it is easily interfered by various environmental sounds. To solve this problem, by using sound signal processing, feature extraction, pattern matching, and other techniques, this study proposes a device sound fault monitoring scheme that is resistant to multiple environmental sound disturbances. First of all, the normal and faulty sounds of transformers in various ambient sounds are collected and preprocessed. Then, MFCC features are extracted and dimensionality is reduced. Next, the normal working sound characteristics of the transformer are trained through the OPTICS algorithm to obtain a standard set with multiple clusters. Last, the standard set is matched with the test sample containing the faulty sound. If there is a mismatch, but the manual test is a false positive, it will be classified as a new cluster. The experimental results show that the proposed method can not only identify the sample well, but also optimize the standard set through the standard set enhancement module when the new normal sound appears, thus improving the recognition accuracy and reducing the false alarm rate.