基于音频特征聚类算法的故障检测方法研究  

Research on Fault Detection Method Based on Audio Feature Clustering Algorithm

作　　者：黄羽 周伟[1] 甘兴 谢晓琰 黄鑫宇 HUANG Yu;ZHOU Wei;GAN Xing;XIE Xiaoyan;HUANG Xinyu(College of Nuclear Technology and Automation Engineering,Chengdu University of Technology,Chengdu 610059,China)

机构地区：[1]成都理工大学核技术与自动化工程学院,成都610059

出　　处：《计算机工程与应用》2023年第15期281-289,共9页Computer Engineering and Applications

基　　金：四川省重点研发计划(2021YFG0075)。

摘　　要：针对工业生产中设备故障在线智能检测的迫切需要,提出了一种基于音频特征深度卷积神经网络和聚类算法联合优化的故障检测方法(fault detection method on the strength of the joint optimization of audio feature deep convolutional neural network and clustering algorithm,FDM_DCNN-CA)。该方法详细阐述了三维坐标系中的特征位置归一化提取和音频信息融合聚类分析等关键技术要点。FDM_DCNN-CA在具有不同背景噪声的音频数据集MIMⅡ上进行了测试,结果表明即使在检测难度较大的“阀门”设备上,仍然取得了良好应用结果。“阀门”设备总体AUC分数超过91%,并且在异常音频信号判断上具有更好的鲁棒性,正确率超过95%。Aiming at the urgent need for online intelligent detection of equipment faults in industrial production,this paper proposes a fault detection method on the strength of the joint optimization of audio feature deep convolutional neural network and clustering algorithm(FDM_DCNN-CA).The key techniques such as feature location extraction and audio information fusion to clustering analysis in the 3D coordinate system are further elaborated in this paper.The FDM_DCNN-CA has been tested on MIMII,an audio dataset with different background noise,and the results show that the FDM_DCNN-CA achieves good results even on the“valve”equipment which is difficult to be detected.The“valve”equipment has an overall AUC score of more than 91%and is more robust to abnormal audio signals,with an accuracy rate of more than 95%.

关 键 词：故障检测 音频特征 无监督学习 三维k-均值聚类 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]