摘要
高压辊磨机的运行环境复杂且信号易受到噪声污染,针对传统算法难以有效提取高压辊磨机故障特征以及随机共振系统参数选取困难的问题,提出了一种基于参数寻优自适应重构特征的高压辊磨机运行故障诊断方法.首先,采用集合经验模态分解(ensemble empirical mode decomposition,EEMD)算法将高压辊磨机振动信号分解成若干个本征模态函数(intrinsic mode function,IMF)分量;其次,结合相关系数与互信息构建混合判别准则,自适应地筛选出异常运行特征最强的分量信号进行重构;在此基础上,引入具有种群概率突变机制的樽海鞘群算法(salp swarm algorithm,SSA),构建自适应的随机共振(stochastic resonance,SR)参数寻优策略;最后,提出基于自适应选取分量重构信号的高压辊磨机运行故障诊断方法.仿真实验结果表明了所提方法的有效性.
The operating environment of the high-pressure grinding roll is complicated,and the signal is easily polluted by noise.Traditional algorithms find it difficult to extract the fault characteristics of high-pressure grinding rolls effectively and select the parameters of the stochastic resonance system.To address these issues,an operation fault diagnosis method of highpressure grinding roll based on adaptive reconstruction features with parameter optimization was proposed.First,the ensemble empirical mode decomposition(EEMD)method was employed to decompose the high-pressure grinding roll’s vibration signal into several intrinsic mode function(IMF)components.Secondly,the mixed criterion of correlation coefficient and mutual information was used to adaptively screen the component signals with the strongest abnormal operation characteristics and reconstruct them.Then,the salp swarm algorithm(SSA)was introduced to build the adaptive stochastic resonance(SR)parameter optimization mechanism by combining the population probabilistic mutation mechanism.Finally,an operation fault diagnosis algorithm of high-pressure grinding roll based on an adaptively selected component reconstruction signal was proposed.Simulation results verify the effectiveness of the proposed method.
作者
孙洪硕
张丹威
徐泉
柴天佑
SUN Hong-shuo;ZHANG Dan-wei;XU Quan;CHAI Tian-you(State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819,China;Jiuquan Iron and Steel(Group)Co.,Ltd.,Jiayuguan 735100,China;National Engineering Research Center of Metallurgy Automation,Northeastem University,Shenyang 110819,China)
出处
《东北大学学报(自然科学版)》
北大核心
2025年第11期1-11,共11页
Journal of Northeastern University(Natural Science)
基金
中央高校基本科研业务费专项资金资助项目(N2324003-05).
关键词
故障诊断
集合经验模态分解
樽海鞘群算法
随机共振
自适应策略
fault diagnosis
ensemble empirical mode decomposition
salp swarm algorithm
stochastic resonance
adaptive strategy
