基于GMPE和GWO-MKELM算法的往复压缩机轴承故障诊断  

Fault Diagnosis of Reciprocating Compressor Bearings Based on GMPE and GWO-MKELM Algorithms

作　　者：李彦阳[1,2] 王金东 曲孝海 LI Yan-yang;WANG Jin-dong;QU Xiao-hai(College of Civil Engineering and Water Conservancy Institute,Heilongjiang Bayi Agricultural University,Daqing 163319,China;College of Mechanical Science and Engineering,Northeast Petroleum University,Daqing 163318,China;College of Mathematics and Science,Hunan University of Arts and Sciences,Changde 415000,China)

机构地区：[1]黑龙江八一农垦大学土木水利学院,大庆163319 [2]东北石油大学机械科学与工程学院,大庆163318 [3]湖南文理学院数理学院,常德415000

出　　处：《科学技术与工程》2024年第23期9842-9847,共6页Science Technology and Engineering

基　　金：湖南文理学院科学研究项目(22ZD08);常德市科技创新指导性项目(2023ZD14)。

摘　　要：针对往复压缩机内部结构复杂,轴承间隙故障特征提取困难和识别准确率不高等问题,提出了多尺度排列熵和多核极限学习机混合算法的智能诊断新方法。首先,针对多尺度排列熵在多尺度过程中,利用均值粗粒化的方式在一定程度上“中和”了原始信号的动力学突变行为,降低了熵值分析的准确性,提出了一种广义多尺度排列熵算法;然后,为解决核极限学习机处理复杂数据样本分类存在的局限性,将高斯核函数、多项式核函数和感知器核函数进行线性叠加,构建混合核函数,提出了多核极限学习机模型。仿真实验结果表明,该故障诊断方法识别准确率高达98%,高效地实现了轴承不同种类故障的智能诊断。A new intelligent diagnosis method based on a hybrid algorithm of multi-scale permutation entropy and multi-core limit learning machine was proposed to address the complex internal structure of reciprocating compressors,difficulties in extracting bearing clearance fault features,and low recognition accuracy.Firstly,a generalized multi-scale permutation entropy(GMPE)algorithm was proposed to solve the problem that the mean coarse-grained method of multi-scale permutation entropy in the multi-scale process“neutralized”the dynamic mutation behavior of the original signal to a certain extent and reduced the accuracy of entropy analysis.Then,in order to solve the limitations of kernel extreme learning machine in dealing with complex data sample classification,Gaussian kernel function,polynomial kernel function and perceptron kernel function were linearly superimposed to construct a hybrid kernel function,and a multiple kernel extreme learning machine(MKELM)model was proposed.The simulation results show that the fault diagnosis accuracy of the proposed method is as high as 98%,and the intelligent diagnosis of different types of bearing faults is realized efficiently.

关 键 词：往复压缩机 灰狼优化算法 广义多尺度排列熵 多核极限学习机 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]