计算技术与自动化2024，Vol.43Issue(4)：41-45,152,6.DOI:10.16339/j.cnki.jsjsyzdh.202404007
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
摘要
Abstract
Due to the difficulty in accessing some equipment in pumped storage power plants,it is difficult to collect ef-fective 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 de-vice,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 fea-ture mining algorithm for anomaly data mining on denoised datasets,combining the mining results with voiceprint data pro-cessing 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Key words
small sample anomaly feature mining algorithm/voiceprint recognition technology/pumped storage power plants/fault detection/voiceprint acquisition sensor分类
建筑与水利引用本文复制引用
于潇,李世昌,卢彬..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[J].计算技术与自动化,2024,43(4):41-45,152,6.基金项目
国网新源公司科技项目(SGXYKJ-2021-007) （SGXYKJ-2021-007）
