基于MEA优化BP神经网络的农机滚动轴承故障诊断  

Fault Diagnosis for Rolling Bearing of Agricultural Mechanical Based on BP Neural Network Optimized by Mind Evolutionary Algorithm

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作  者:唐立力[1] 陈国彬[1] Tang Lili;Chen Guobin(Rongzhi College, Chongqing Technology and Business University, Chongqing 401320, China)

机构地区:[1]重庆工商大学融智学院,重庆401320

出  处:《农机化研究》2019年第3期214-218,共5页Journal of Agricultural Mechanization Research

基  金:重庆市教委科学技术研究项目(KJ1601903).

摘  要:为了解决农机滚动轴承的故障诊断问题,提出了一种基于思维进化算法(MEA)优化BP神经网络的故障诊断新方法。该方法利用思维进化算法的趋同和异化操作,通过竞争获取优胜种群,在迭代过程中不断优化BP神经网路的初始权值和阈值,建立MEA-BP网络农机滚动轴承故障诊断模型。以滚动轴承试验实测数据为例,通过MatLab软件进行仿真,结果证实:该方法不但克服了常规BP网络学习速度慢和局部极小的缺点,而且提高了故障诊断准确度,为其他农业机械设备的故障诊断提供了一种试验方法。In order to solve the fault diagnosis problem of rolling bearing of agricultural mechanical. This paper presents a new method for fault diagnosis based on BP neural network optimized by mind evolutionary algorithm (MEA). This method uses the similartaxis and dissimilation of mind evolutionary algorithm to obtain the winning population through competition, the initial weights and biases of the BP neural network are continuously optimized during the iteration process, and then establishing the fault diagnosis model for rolling bearing of agricultural mechanical based on MEA-BP network. Taking the rolling bearing measured data of test as an example, this method can not only overcome the defects of BP neural network which has slow learning speed and local minimum, but also improve the accuracy of fault diagnosis in simulation results by MATLAB software. It provides a test method for the fault diagnosis of other agricultural mechanical equipment.

关 键 词:农机滚动轴承 故障诊断 思维进化算法 BP神经网络 

分 类 号:S220.3[农业科学—农业机械化工程;农业科学—农业工程] TP133.33

 

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