基于小样本异常特征挖掘算法的抽水蓄能电站主设备故障声纹检测技术  

Voiceprint Detection Technology for Main Equipment Faults in Pumped Storage Power Plants Based on Small Sample Anomaly Feature Mining Algorithm

作　　者：于潇 李世昌 卢彬 YU Xiao;LI Shichang;LU Bin(Hebei Zhanghewan Energy Storage Power Generation Co.,Ltd.,Shijiazhuang,Hebei 050001,China)

机构地区：[1]河北张河湾蓄能发电有限责任公司,河北石家庄050001

出　　处：《计算技术与自动化》2024年第4期41-45,152,共6页Computing Technology and Automation

基　　金：国网新源公司科技项目(SGXYKJ-2021-007)。

摘　　要：由于抽水蓄能电站中部分设备难以接近,导致采集有效信号的难度较大,为了降低设备故障检测中的漏检率与误检率,将小样本异常特征挖掘算法与声纹识别技术结合,提出一种新的抽水蓄能电站主设备故障检测方法。选取压电式加速度传感器作为声纹采集传感器设备,设定相关参数,并通过Pearson相关系数确定传感器布设位置,借助声纹数据采集模块采集运行状态数据。将EMD与小波变换结合对采集得到的主设备运行状态数据实施去噪,降低故障检测误差。采用小样本异常特征挖掘算法针对去噪数据集进行异常数据挖掘,将挖掘结果与声纹数据处理结合实现主设备故障检测。实验结果表明,所提方法不仅降低了故障检测的漏检率与误检率,而且能够克服干扰信号的影响,实用价值较高。Due to the difficulty in accessing some equipment in pumped storage power plants,it is difficult to collect effective signals.In order to reduce the missed and false detection rates in equipment fault detection,a new fault detection method for main equipment in pumped storage power plants is proposed by combining small sample anomaly feature mining algorithm with voiceprint recognition technology.Select a piezoelectric accelerometer as the voiceprint acquisition sensor device,set relevant parameters,and determine the sensor placement position through Pearson correlation coefficient.Use the voiceprint data acquisition module to collect operating status data.Combining EMD with wavelet transform to denoise the collected operational status data of the main equipment and reduce fault detection errors.Using a small sample anomaly feature mining algorithm for anomaly data mining on denoised datasets,combining the mining results with voiceprint data processing to achieve main device fault detection.The experimental results show that the proposed method not only reduces the missed detection rate and false detection rate of fault detection,but also overcomes the influence of interference signals,with high practical value.

关 键 词：小样本异常特征挖掘算法 声纹识别技术 抽水蓄能电站 故障检测 声纹采集传感器 

分 类 号：TV735[水利工程—水利水电工程]