摘要
源分离算法的分离性能受到分离矩阵的影响,不能自适应地分离滚动轴承的复合故障特征,针对这一问题,将自适应果蝇优化算法(AFOA)与降噪源分离(DSS)相结合,提出了一种基于AFOA算法的滚动轴承复合故障降噪源分离方法。首先,利用自适应果蝇优化算法对分离矩阵进行了初步优化,再将分离矩阵作为果蝇个体,负熵作为目标函数,对目标函数最大值进行了全局寻优,进而确定了降噪源分离的最优分离矩阵;以正切函数作为降噪源分离的降噪函数,对内、外圈复合故障轴承振动信号进行了估计;最后,进行了包络分析,提取出了轴承内、外圈故障特征;此外,通过仿真和实测轴承内、外圈复合故障试验,将所提方法与基于自适应果蝇优化算法的快速独立成分分析进行了对比。研究结果表明:AFOA-DSS方法能够更精确分离滚动轴承的复合故障特征,实现对轴承复合故障的诊断;该方法的有效性和实用性得到了验证。
Aiming at the problem that the separation performance of the source separation algorithm was affected by the separation matrix and the compound fault characteristics of rolling bearings could not be adaptively separated,the adaptive fruit fly optimization algorithm and the denoising source separation were combined,a method of denoising source separation for rolling bearing composite faults based on AFOA algorithm was proposed.First,adaptive fruit fly optimization algorithm was used to initially optimize the separation matrix,and then the separation matrix was taken as the individual fruit fly and negative entropy was taken as the objective function,global optimization on the maximum value of the objective function was carried out.And the optimal separation matrix for denoising source separation was determined.The tangent function was used as noise reduction function for noise redution separation to estimate the vibration signal of the inner and outer ring compound fault bearings.Finally,the envelope analysis was performed to extract the inner and outer ring fault characteristics.In addition,the proposed method was compared with fast independent component analysis based on the adaptive fruit fly optimization algorithm,through the simulation and actual test of the compound faults of the inner and outer rings of the bearing.The results show that the AFOA-DSS method can more accurately separate the compound faults of the rolling bearing features,realize fault diagnosis,verify the effectiveness and practicability of the method.
作者
刘畅
金京
王衍学
LIU Chang;JIN Jing;WANG Yan-xue(School of Mechanical-Electronic and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《机电工程》
CAS
北大核心
2021年第6期681-688,共8页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(51875032,61463010,51475098)
广西自然科学杰出青年基金资助项目(2016GXNSFFA380008)。
关键词
滚动轴承
复合故障诊断
自适应果蝇优化算法
降噪源分离
负熵
快速独立成分分析
rolling bearing
compound fault diagnosis
adaptive fruit fly optimization algorithm(AFOA)
denoising source separation(DSS)
negative entropy
fast independent component analysis(Fast ICA)
