文章摘要
刘利平,蒋柳成,乔乐乐,孙建,高世妍.基于时频分帧能量熵的陶瓷制品敲击声波信号特征识别*[J].,2023,42(1):57-66
基于时频分帧能量熵的陶瓷制品敲击声波信号特征识别*
Coin-tap sound signal characteristics recognition of ceramic products based on time-frequency framing energy entropy
投稿时间:2021-11-26  修订日期:2022-12-23
中文摘要:
      针对现有陶瓷制品敲击声波信号特征提取方法中提取的特征代表性降低的问题,该文提出结合最大重叠离散小波包变换(MODWPT)和时频分帧能量熵的特征提取方法。首先采用MODWPT将信号分解为4层,再对每个节点的子信号分帧后计算各个节点的时频分帧能量熵,然后根据能量分布特征选择了前6个节点的时频分帧能量熵特征,最后构建随机森林分类器完成识别,将该方法和MODWPT时频分段能量熵、MODWPT归一化能量特征2种方法进行比较。实验结果表明,相比MODWPT时频分段能量熵、MODWPT归一化能量两种特征提取方法,MODWPT时频分帧能量熵能提升特征的代表性,具有更优的陶瓷制品敲击声波信号特征识别性能,其识别的F1值达到了98.46%,相比上述两种方法分别提升F1值3.22%、1.86%。
英文摘要:
      In view of the problems of decreasing of extracted feature representativeness in feature extraction method of ceramic products coin-tap sound signal characteristics Recognition, the feature extraction method combining maximum overlap discrete wavelet transformation (MODWPT) with time-frequency framing energy entropy is proposed. Firstly, the MODWPT method is applied to decompose the coin-tap sound signal to level 4. Then the time-frequency framing energy entropy of each node is calculated after sub-signal framing of each node. And then the time-frequency framing energy entropy features of the first 6 nodes are selected according to the energy distribution feature. Finally, the random forest (RF) classifier is constructed to complete the identification. This method is compared with two methods of MODWPT time-frequency segment energy entropy and MODWPT normalized energy features. The experimental results show that compared with MODWPT time-frequency segment energy entropy and MODWPT normalized energy, MODWPT time-frequency framing energy entropy can improve the representativeness of extracted feature, and has better recognition performance of coin-tap acoustic signal feature of ceramic products. The F1 value of recognition reached 98.5%, and compared with the above two methods, 3.1% and 1.9% are increased respectively.
DOI:10.11684/j.issn.1000-310X.2023.01.008
中文关键词: 敲击法  最大重叠离散小波包变换  时频分帧能量熵  模式识别
英文关键词: MODWPT  coin-tap  time-frequency framing energy entropy  pattern recognition
基金项目:河北省省级科技计划(20327218D);华北理工大学研究生创新项目课题(2019B28)
作者单位E-mail
刘利平 华北理工大学人工智能学院 华北理工大学矿业工程学院 11745430@qq.com 
蒋柳成* 华北理工大学人工智能学院 3448599779@qq.com 
乔乐乐 华北理工大学人工智能学院 qiaolele2020@163.com 
孙建 华北理工大学人工智能学院 河北 1125439094@qq.com 
高世妍 华北理工大学人工智能学院 gaoshiyan@stu.ncst.edu.cn 
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