基于音频数据的风电机组小样本异常检测技术研究  

Research on Few Shot Anomaly Detection Technique for Wind Turbines Based on Audio Data

作　　者：刘悦 罗潇 褚伟 LIU Yue;LUO Xiao;CHU Wei(CRRC Zhuzhou Institute Co.,Ltd.,Zhuzhou,Hunan 412001,China)

机构地区：[1]中车株洲电力机车研究所有限公司,湖南株洲412001

出　　处：《控制与信息技术》2024年第2期57-62,共6页CONTROL AND INFORMATION TECHNOLOGY

摘　　要：目前风电机组(简称“风机”)异常往往是靠经验丰富的运维人员通过“听声辨音”的方法来鉴别的,效率低且及时性差。声学故障检测具有安全性、连续性和稳定性的特点,但其在风机异常检测方面的应用研究尚处于起步阶段。为帮助运维人员快速诊断故障,文章提出一种基于音频数据的风机小样本异常检测方法。其首先构建了一套风机音频故障检测系统,并采用降噪循环神经网络(RNNoise)对所采集的风机故障音频数据进行预处理;然后针对风力发电机组故障有效样本信息少、故障样本中存在伪样本的情况,采用压缩重构异常检测方法实现小样本异常检测;最后通过异常数据的反向梯度传递进一步区分伪故障,从而提高风机故障检测准确率。实际数据测试结果显示,该方法对风机偏航异响识别率超95%,可满足风场本地部署需求,进一步提升风场智能化技术水平。At present,The predominant method of anomaly detection for wind turbines remains manual"sound inspection"conducted by experienced operation and maintenance personnel,despite its low efficiency and poor timeliness.The application research on wind turbines anomaly detection is still in its early stages,despite the inherent characteristics of security,continuity,and stability associated with acoustic fault detection.Recognizing the audio fault detection characteristics,this paper presents a few shot anomaly detection method for wind turbines based on audio data,to support quick diagnosis by operation and maintenance staff.Firstly,a wind turbine audio fault detection system is established and the collected wind turbine fault audio data is preprocessed using a recurrent neural network(RNNoise).Then,in view of the deficiency of effective samples of wind turbine faults and the presence of pseudo samples,the compression and reconstruction anomaly detection method was used to facilitate few shot anomaly detection.Finally,pseudo faults were further distinguished by the reverse gradient transfer of anomaly data,leading to an enhancement of fault detection accuracy.Actual data collected in an experiment show a more than 95%recognition rate for abnormal sound arising from wind turbine yaw,demonstrating the viability of the proposed technique for deployment at wind farms.This application contributes to improving the intelligent technical level of wind farms.

关 键 词：风电机组 异常检测 音频数据 深度学习 小样本 

分 类 号：O436[机械工程—光学工程]