基于BAS优化PNN网络的电机轴承故障诊断方法  

Fault Diagnosis Method of Motor Bearing Based on PNN Optimized by BAS Algorithm

作　　者：刘霞[1] 王鑫宇 路敬祎[1] 李其浩 LIU Xia;WANG Xinyu;LU Jingyi;LI Qihao(College of Electrical Engineering and Information,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2021年第4期439-444,共6页Journal of Jilin University（Information Science Edition）

基　　金：黑龙江省自然科学基金资助项目(LH2020F005);东北石油大学青年科学基金资助项目(2018QNL-33)。

摘　　要：针对电机轴承故障识别准确率不高问题,提出了一种天牛须搜索算法(BAS:Beetle Antennae Search)与概率神经网络(PNN:Probabilistic Neural Network)相结合的滚动轴承故障诊断方法。该方法结合LLE(Locally Linear Embedding)算法得到振动信号的敏感特征,保证振动信号的可靠性和敏感性。并采用天牛须搜索算法对PNN网络中的平滑参数进行寻优,避免主观经验选取参数对诊断结果的影响。通过实验验证了该方法的有效性,可实现故障类型准确判别。Aiming at the problem of motor bearing failure identification problems,a fault diagnosis method for motor bearings based on BAS(Beetle Antennae Search)algorithm and PNN(Probabilistic Neural Network)is proposed.The LLE(Locally Linear Embedding)algorithm is used to obtain the sensitive characteristics of the vibration signal to ensure the reliability and sensitivity of the vibration signal.The Beetle Antennae Search algorithm is used to find the optimal smoothing factor in the model to avoid the influence of subjective empirical selection on the diagnosis results.The experimental results show the effectiveness of the method,and it can accurately identify the fault type.

关 键 词：电机轴承 故障诊断 天牛须搜索算法 概率神经网络 局部线性嵌入 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]