抗环境音干扰的设备声音故障监测方法

变压器等大型设备在运行过程中发声具有辨识性和平稳性的特点，但容易受各种环境音的干扰，针对该问题，本文利用声音信号处理、特征提取、模式匹配等技术，提出了一种抗多种环境音干扰的设备声音故障监测方案.首先对在各种环境声音中变压器的正常和故障声音进行采集和预处理，然后对其提取出MFCC特征并降维，对变压器正常工作声音特征通过OPTICS算法进行训练，得到一个具有多个分类的标准集，最后将标准集与包含故障声音的测试样本进行匹配，若出现不匹配情况但经人工检验为误报，则将其归为新的分类.实验结果表明：该方法不仅能很好的识别样本，也能在新的正常声音出现时通过标准集增强模块来优化标准集，从而提高识别准确率并降低误警率.

Device Sound Fault Monitoring Method of Anti-Environmental Sound Interference

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.