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声纹的变压器放电与机械故障诊断研究*

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吴国鑫,詹花茂,李敏.声纹的变压器放电与机械故障诊断研究*[J].,2021,40(4):602-610
声纹的变压器放电与机械故障诊断研究*
Research on transformer discharge and mechanical fault diagnosis based on voiceprint
投稿时间：2021-02-26修订日期：2021-07-03
中文摘要:
变压器中的一些放电和机械故障会产生异常声音,可用于故障检测。据此,本文提出基于可听声的变压器放电和机械故障诊断方法。针对机械故障声音与变压器本体噪声特征相似易混淆的问题提出改进小波包-BP神经网络算法,与传统小波包-BP神经网络算法相比声音的识别率提高了5.7%。为提高声音识别系统的泛化性,提出基于梅尔对数频谱和卷积神经网络的声音识别算法。两种算法相互验证,提高了系统的可靠性。在真实变压器油箱中模拟了不同类型放电和机械故障。试验结果表明,本文提出的两种方法能成功识别放电和机械故障的声音,声音识别率分别为99.6％和97.57％。
英文摘要:
Some discharges and mechanical faults in the transformer will produce abnormal sounds, which can be used for fault detection. Accordingly, a transformer fault diagnosis method based on audible is proposed. Aiming at the problem that the sound of mechanical faults is similar to the noise of the transformer itself and is easy to be confused, an improved wavelet packet-BP neural network algorithm is proposed. Compared with the traditional wavelet packet-BP neural network algorithm, the sound recognition rate is increased by 5.7%. In order to improve the generalization of the sound recognition system, a sound recognition algorithm based on log-Mel spectrum and convolutional neural network is proposed. The two algorithms are mutually verified, which improves the reliability of the system. Different types of discharges and mechanical failures are simulated in the real transformer tank. The test results show that the two methods proposed in this paper can successfully identify the sounds of discharge and mechanical failure, and the sound recognition rates are 99.6% and 97.57%, respectively.
DOI：10.11684/j.issn.1000-310X.2021.04.015
中文关键词:变压器可听声小波包BP神经网络卷积神经网络
英文关键词:transformeraudible soundwavelet packetbp neural networkconvolutional neural network
基金项目:北京市自然科学基金资助项目
作者单位E-mail
吴国鑫华北电力大学wugxgz@163.com
詹花茂华北电力大学zhanhm@ncepu.edu.cn
李敏华北电力大学li_min1015@126.com
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