基于思维进化优化极限学习机的滚动轴承故障的智能诊断
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Keywords: mind evolutionary algorithm ； extreme learning machine ； rolling bearing ； fault diagnosis
滚动轴承作为机械设备的基础配件，其工作状态 对整台设备的正常运行往往具有直接影响，一旦出现
故障将会造成安全隐患和重大经济损失⑷，因此对轴 承故障的诊断与预测越来越受到人们的关注。
检测与88量 2°勺9年第"期__________________________________________________________________________ TestandQualily
基* 于思维进化优化极限学习机的滚动轴承故障的智能诊断
马丽华恥朱春梅②赵西伟②覃绘桥恥 (①北京信息科技大学机电工程学院，北京100192；②现代测控技术教育部重点实验室，北京100192)
Beijing 100192,CHN ；©Key Laboratory of Modem Measurement and Control Technology of Education, Beijing 100192,CHN)
Abstract: In order to solve the problem that the input weights and thresholds generated by the traditional extreme learning machine have a great impact on the accuracy of fault diagnosis, an intelligent diagnosis method using Mind Evolutionary Algorithm ( MEA) to optimize the Extreme Learning Machine ( ELM) is pro posed. First, the ELM method of weight and threshold value be encoded into individual MEA algorithm, generating initial population, and then through assimilation and dissimilation competition to produce su perior species of operation is completed, the whole iterative process continuously optimize the initial weights and threshold value of extreme learning machine, finally to obtain the optimal individual, to the best individual decoding to obtain the optimum input power and threshold value w b.Will build MEA ELM fault diagnosis model is applied to fault diagnosis of rolling bearing, and respectively with traditional extreme learning machine fault diagnosis model and comparing the experimental results of BP neural network model, the results show that the optimized MEA of ELM not only maintained the charac teristic of fast classification, and effectively improve the diagnostic accuracy, prove the feasibility and ef fectiveness of the proposed method .
经过MEA优化后的ELM不但保持了分类速度快的特点，而且有效提高了诊断的准确率，证明所提
出的方法具有良好的可行性和有效性。
Байду номын сангаас
关键词：思维进化算法;极限学习机;滚动轴承;故障诊断
中图分类号:TP206+.3
文献标识码:A
DOI： 10.19287/j. cnki. 1005-2402.2019.11.021
Fault diagnosis for rolling bearing based on mind evolutionary algorithm optimizes extreme learning machine
MA Lihua®®, ZHU Chunmei②，ZHAO Xiwei②，QIN Huiqi&o①② (①Mechanic and Electronic Engineering College, Beijing Information Science and Technology University ,
成MEA算法个体,产生初始种群，然后通过趋同和异化操作完成种群间的竞争产生优胜种群 ，整个
迭代过程中不断优化极限学习机的初始权值和阈值，最后获得最优个体，对最优个体解码获得隐含
层的最优输入权值卩和阈值几 将建立的MEA-ELM故障诊断模型应用于滚动轴承的故障诊断
中，并分别与传统极限学习机故障诊断模型以及 BP神经网络模型的实验结果进行对比,结果表明,
摘要:为了解决传统极限学习机随机产生的输入权值和阈值对故障诊断的准确率有较大影响的问题 ，提出
了一种利用思维进化算法(mind evolutionary algorithm, MEA)来优化极限学习机(extreme
learning machine,ELM)的智能诊断方法。MEA-ELM方法首先将ELM的权值w和阈值b编码
滚动轴承作为机械设备的基础配件，其工作状态 对整台设备的正常运行往往具有直接影响，一旦出现
故障将会造成安全隐患和重大经济损失⑷，因此对轴 承故障的诊断与预测越来越受到人们的关注。
检测与88量 2°勺9年第"期__________________________________________________________________________ TestandQualily
基* 于思维进化优化极限学习机的滚动轴承故障的智能诊断
马丽华恥朱春梅②赵西伟②覃绘桥恥 (①北京信息科技大学机电工程学院，北京100192；②现代测控技术教育部重点实验室，北京100192)
Beijing 100192,CHN ；©Key Laboratory of Modem Measurement and Control Technology of Education, Beijing 100192,CHN)
Abstract: In order to solve the problem that the input weights and thresholds generated by the traditional extreme learning machine have a great impact on the accuracy of fault diagnosis, an intelligent diagnosis method using Mind Evolutionary Algorithm ( MEA) to optimize the Extreme Learning Machine ( ELM) is pro posed. First, the ELM method of weight and threshold value be encoded into individual MEA algorithm, generating initial population, and then through assimilation and dissimilation competition to produce su perior species of operation is completed, the whole iterative process continuously optimize the initial weights and threshold value of extreme learning machine, finally to obtain the optimal individual, to the best individual decoding to obtain the optimum input power and threshold value w b.Will build MEA ELM fault diagnosis model is applied to fault diagnosis of rolling bearing, and respectively with traditional extreme learning machine fault diagnosis model and comparing the experimental results of BP neural network model, the results show that the optimized MEA of ELM not only maintained the charac teristic of fast classification, and effectively improve the diagnostic accuracy, prove the feasibility and ef fectiveness of the proposed method .
经过MEA优化后的ELM不但保持了分类速度快的特点，而且有效提高了诊断的准确率，证明所提
出的方法具有良好的可行性和有效性。
Байду номын сангаас
关键词：思维进化算法;极限学习机;滚动轴承;故障诊断
中图分类号:TP206+.3
文献标识码:A
DOI： 10.19287/j. cnki. 1005-2402.2019.11.021
Fault diagnosis for rolling bearing based on mind evolutionary algorithm optimizes extreme learning machine
MA Lihua®®, ZHU Chunmei②，ZHAO Xiwei②，QIN Huiqi&o①② (①Mechanic and Electronic Engineering College, Beijing Information Science and Technology University ,
成MEA算法个体,产生初始种群，然后通过趋同和异化操作完成种群间的竞争产生优胜种群 ，整个
迭代过程中不断优化极限学习机的初始权值和阈值，最后获得最优个体，对最优个体解码获得隐含
层的最优输入权值卩和阈值几 将建立的MEA-ELM故障诊断模型应用于滚动轴承的故障诊断
中，并分别与传统极限学习机故障诊断模型以及 BP神经网络模型的实验结果进行对比,结果表明,
摘要:为了解决传统极限学习机随机产生的输入权值和阈值对故障诊断的准确率有较大影响的问题 ，提出
了一种利用思维进化算法(mind evolutionary algorithm, MEA)来优化极限学习机(extreme
learning machine,ELM)的智能诊断方法。MEA-ELM方法首先将ELM的权值w和阈值b编码