首页|基于粒子群优化ACMD方法的滚动轴承复合故障分离方法

基于粒子群优化ACMD方法的滚动轴承复合故障分离方法

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  • 国家科技期刊平台
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为了对强背景噪声干扰下的滚动轴承复合故障特征进行提取,课题组提出一种基于粒子群和自适应调频模式分解(adaptive chirp mode decomposition,ACMD)的滚动轴承复合故障分离的特征提取方法.首先,构建一个复合故障分解因子(compound fault decomposition factor,CFDF)用于评价复合故障特征提取效果;然后,将最大复合故障分解因子作为目标函数,利用粒子群寻优算法自适应搜索ACMD最优参数,进而实现信号模态分解;最后,对分解后的多模态分量进行平方包络谱分析,进而判断轴承的故障类型.仿真及试验结果表明:该方法能够实现强背景噪声干扰下的滚动轴承复合故障特征提取,分离出单一的故障信息.对比经典VMD方法,该方法具有更好的鲁棒性.
Compound Fault Separation Method for Rolling Bearings Based on Particle Swarm Optimization ACMD Method
To solve the problem of feature extraction of rolling bearing complex fault in the presence of strong background noise,a feature extraction method of rolling bearing complex fault separation was proposed based on particle swarm analysis and adaptive chirp mode decomposition(ACMD).First,a compound fault decomposition factor(CFDF)was constructed to evaluate the compound fault feature extraction effect.Then,the maximum compound fault decomposition factor and was taken as the objective function,and the optimal parameters of ACMD were searched adaptively by particle swarm optimization algorithm,and the signal modal decomposition was realized.Finally,the decomposed multimodal components were analyzed by the square envelope spectrum to determine the bearing fault type.The simulation and experimental results show that the proposed method can realize the feature extraction of rolling bearing complex faults under the interference of strong background noise,and separate a single fault information.Compared with classical VMD method,the proposed method has better robustness.

rolling bearingcompound faultACMD(Adaptive Chirp Mode Decomposition)CFDF(Compound Fault Decomposition Factor)PSO(Particle Swarm Optimization)

张玮、何建国、区瑞坚、薛卓

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西安工程大学机电工程学院,陕西西安 710048

苏州微著设备诊断技术有限公司,江苏苏州 215200

滚动轴承 复合故障 自适应调频模式分解(ACMD) 复合故障分解因子(CFDF) 粒子群算法(PSO)

陕西省科技厅自然科学基础研究计划面上项目

2022JM-219

2024

10.3969/j.issn.1005-2895.2024.02.014

轻工机械
中国轻工机械协会,中国轻工业机械总公司,轻工业杭州机电设计研究院

轻工机械

CSTPCD
影响因子：0.465
ISSN：1005-2895
年,卷(期)：2024.42(2)
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