基于改进贝叶斯网络的电机轴承故障诊断  

Motor bearing fault diagnosis based on improved Bayesian network

作　　者：仝兆景[1] 李金香 乔征瑞 芦彤 Tong Zhaojing;Li Jinxiang;Qiao Zhengrui;Lu Tong(School of Electrical Engineering&Automation,Henan Polytechnic University,Jiaozuo 454003,China;State Grid Henan Baoquan Pumped Storage Co.,Ltd.,Xinxiang 453636,China)

机构地区：[1]河南理工大学电气工程与自动化学院,焦作454003 [2]河南国网宝泉抽水蓄能有限公司,新乡453636

出　　处：《电子测量技术》2022年第7期48-55,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(U1504623)项目资助。

摘　　要：针对电机轴承振动信号受噪声干扰影响特征提取和传统贝叶斯网络故障诊断准确率低的问题,提出一种基于改进贝叶斯网络的电机轴承故障诊断方法。采用自适应噪声集合模态分解的方法对数据进行降噪处理,增加了模型的鲁棒性;采用差分进化和模拟退火算法对蝗虫算法进行优化,增强蝗虫算法的全局和局部搜索能力;将优化后的蝗虫算法应用于贝叶斯网络结构学习构建轴承故障诊断模型;通过实验对比证明,该方法对轴承的多故障分类具有更强的学习能力和更高的准确率,实验对部分样本的故障诊断率达到97.15%,平均准确率达到98.73%。A motor bearing fault diagnosis model based on an improved Bayesian network is proposed for the problem that the motor bearing vibration signal is affected by noise interference in feature extraction and low accuracy of traditional Bayesian network fault diagnosis.The adaptive ensemble modal decomposition of noise method is employed for noise reduction of data,which increases the model robustness.The Grasshopper algorithm is optimized by using differential evolution and simulate anneal algorithm to enhance the global and local search ability of the Grasshopper algorithm.The optimized Grasshopper algorithm is applied to the Bayesian network structure and learning to construct the bearing fault diagnosis model.Through comparing with other methods,it is proved that the method has stronger learning ability and higher accuracy rate for multi-fault classification of bearings,and the experiment fault diagnosis for some samples result reaches 97.15%and the average accuracy rate can reach 98.73%.

关 键 词：轴承 故障诊断 自适应噪声模态分解 蝗虫算法 贝叶斯网络 

分 类 号：TH183[机械工程—机械制造及自动化]