摘要
为了降低声纹识别算法在低信噪比环境下的误报率,提出了一种基于快速增量式支持向量数据描述(fast incremental support vector data description,FISVDD)以及门控循环单元(gate recurrent unit,GRU)的变压器机械故障声纹识别方法。以变压器为实验对象,分别获取变压器在正常工况、铁芯松动、线圈松动3种状态下的声音信号,并使用Mel频率倒谱系数进行特征提取。FISVDD作为第1级算法分离陌生类,同时通过增量学习的方式学习新样本。GRU作为第2级分类算法,对通过第1级算法的样本进行识别。实验结果表明:与传统闭集识别算法相比,FISVDD需要的训练时间更少;相较于传统的机器学习算法,GRU在变压器音频识别任务中具有更高的识别准确率和抗噪识别能力;所提方法相较于1级算法更加有效,在识别信噪比高于10 dB的样本时召回率下降不超过1%,在识别信噪比不超过0 dB的样本时误报率不超过10%。
In order to reduce the false alarm rate of voiceprint recognition algorithms in environment of low signal-noise ratios,this paper proposed a transformer mechanical fault voiceprint recognition method based on fast incremental support vector data description(FISVDD)and gate recurrent unit(GRU).The transformer was taken as the experimental object,and the sound signals of the transformer under normal working conditions,loose iron core state and loose coil state were obtained,respectively.Mel frequency cepstrum coefficients were used for feature extraction.As the first-level algorithm,FISVDD was used to separate strange classes and learn new samples through incremental learning.As the second-level classification algorithm,GRU was used to identify the samples passing the first-level algorithm.The experimental results show that FISVDD requires less training time than traditional closed-set recognition algorithms.Compared with traditional machine learning algorithms,GRU has higher recognition accuracy and anti-noise recognition ability in the transformer audio recognition task.Compared with the one-level algorithm,the proposed method is more effective.When identifying samples with a signal-noise ratio higher than 10 dB,the recall rate decreases by not more than 1%.When identifying samples with a signal-noise ratio not more than 0 dB,the false alarm rate does not exceed 10%.
作者
王荣昊
李喆
孙正
胡赵宇
孙汉文
江秀臣
WANG Ronghao;LI Zhe;SUN Zheng;HU Zhaoyu;SUN Hanwen;JIANG Xiuchen(Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;SAIC Motor Corporation Limited,Shanghai 200041,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2022年第11期4546-4556,共11页
High Voltage Engineering
