基于ICEEMDAN和PSO-LSSVM的石油机械滚动轴承故障诊断方法研究  

Research on Rolling Bearing Fault Diagnosis Method in Petroleum Machinery Based on ICEEMDAN and PSO-LSSVM

作　　者：郑立朝 宋宏志[1] 顾启林[1] 章宝玲 安宏鑫 张瀚阳 别锋锋[2] ZHENG Lizhao;SONG Hongzhi;GU Qilin;ZHANG Baoling;AN Hongxin;ZHANG Hanyang;BIE Fengfeng(Production Optimization,China Oilfield Services Limited,Tianjin 300459,China;School of Mechanical and Rail Transit,Changzhou University,Changzhou 213164,China)

机构地区：[1]中海油田服务股份有限公司油田生产事业部,天津300459 [2]常州大学机械与轨道交通学院,江苏常州213164

出　　处：《计算机测量与控制》2024年第8期129-137,共9页Computer Measurement &Control

基　　金：国家重点研发计划项目(2021YFB3302104)。

摘　　要：针对滚动轴承疲劳故障振动信号具有能量弱、特征稀疏等特点,提出了一种通过改进自适应噪声完备经验模态分解方法与粒子群优化的最小二乘支持向量机结合的故障识别方法;对轴承不同故障信号利用改进的自适应噪声完备经验模态算法分解为一系列固有模态函数分量;根据相关系数-方差贡献率准则筛选出最能表征原始信号状态的分量,并计算重构分量的奇异谱熵值构成特征向量;将提取的特征向量集合输入到基于粒子群优化的最小二乘支持向量机分类器中,进行模型的训练和故障模式的识别,与SVM和LSSVM分类器模型进行准确率和效率比较;试验结果表明,该方法在滚动轴承故障信号中能有效提取故障特征,准确率达98.75%,具有一定可靠性和实用性。In view of the weak energy and sparse features of fatigue fault vibration signals of rolling bearings,a fault identification method combining improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) and particle swarm optimization least-squares support vector machine(PSO-LSSVM) is proposed.The improved adaptive noise complete empirical mode algorithm is used to decompose different bearing fault signals into a series of inherent modal function(IMF) components;The component that can best represent the original signal state is selected according to the correlation core-variance contribution ratio criterion,and the singular spectrum entropy of the reconstructed component is calculated to form the feature vector;The extracted feature vector set is input into the least square support vector machine classifier based on particle swarm optimization,which trains the model and identifies the fault mode.The accuracy and efficiency of the model are compared with that of the support vector machine(SVM) and least-squares support vector machine(LSSVM) classifier.The test results show that the method can effectively extract fault characteristics from rolling bearing fault signals,with an accuracy of 98.75%,which has certain reliability and practicability.

关 键 词：滚动轴承 ICEEMDAN分解 奇异谱熵 PSO-LSSVM 模式识别 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置] TE933[自动化与计算机技术—控制科学与工程] TH133.33[石油与天然气工程—石油机械设备]