基于声音特征的水力发电机组试验分析

Working Condition Analysis of Hydroelectric Generating Units Based on Acoustic Characteristics

针对工业设备声音数据的易得性,对两河口2号水轮机组开机试验过程中的升/甩负荷等实验进行声音数据采集,对采集的声音数据进行RMS、频谱、声谱图分析。基于波形、频谱以及声谱图的细微差别,选择神经网络作为辅助手段,将声谱图作为训练样本进入神经网络输入层,得到声纹特征,将声纹特征接入聚类模型实现分类,并实现测试样本的分类打分。结果表明,试验中的不同负荷工况和尾水门泄露事故均能够正确识别,本试验训练的模型对工况分类的正确率达到了100%。该研究将有助于建立针对水电站机电设备整体和重要关键部件的机器声纹特征图谱库。

In view of the easy availability of sound data of industrial equipment, the sound data is collected for experiments such as load rejection during the start-up test of a hydraulic unit, and the collected sound data is analyzed by RMS, spectrum and spectrogram. Based on the waveform, spectrum and spectrogram, a neural network is selected as an auxiliary means, and the spectrogram is used as a training sample to enter the input layer of the neural network to obtain voiceprint features to realize the classification and scoring of test samples. The results show that different load conditions and tailgate leakage accidents can be correctly identified in the test. This research will help to establish a machine voiceprint feature map library for the whole and important key components of electromechanical equipment in hydropower stations.