摘要
为解决目前电动剃须刀刀片旋转异响声人工检测效率低、经验要求高的问题,提出一种将小波变换和人工鱼群算法优化的支持向量机相结合的声学检测方法。该方法首先通过离散小波变换对电动剃须刀刀片旋转声信号进行小波分解和重构,将获得的各层相对小波能量作为样本特征参量,然后采用人工鱼群算法对支持向量机进行优化,最后使用优化后的模型对样本进行训练和分类识别。研究结果表明,人工鱼群算法优化的支持向量机在识别率方面优于传统支持向量机,样本识别率可达95%以上。
To solve the problems of low efficiency and high experience requirement in manual detection of the abnormal sound generated by electric shaver blade rotation,an acoustic testing method combining the wavelet transform and the support vector machine(SVM)optimized by artificial fish swarm algorithm is proposed.Firstly,the acoustic signal of the electric shaver blade rotation is decomposed and reconstructed by discrete wavelet transform,and the obtained relative wavelet energy of each layer is used as the characteristic parameters of samples.Secondly,the support vector machine is optimized by the artificial fish swarm algorithm,and the optimized model is used to train samples and perform classification.The research results show that the support vector machine which is optimized by artificial fish swarm algorithm is superior to the traditional support vector machine in terms of recognition accuracy,and the recognition rate of samples reaches 95%.
作者
方杨涛
沈超
郑慧峰
王月兵
FANG Yangtao;SHEN Chao;ZHENG Huifeng;WANG Yuebing(College of Metrology and Measurement Engineering of China Jiliang University,Hangzhou 130022,Zhejiang,China)
出处
《声学技术》
CSCD
北大核心
2021年第4期560-567,共8页
Technical Acoustics
关键词
异响检测
小波变换
人工鱼群算法
支持向量机
abnormal noise detection
wavelet transform
artificial fish swarm algorithm
support vector machine
