摘要
人为因素对传统滚动轴承故障诊断方法有比较大的影响,并且故障起因比较复杂。针对此问题提出用基于量子粒子群(QPSO)算法优化的相关向量机(RVM)进行滚动轴承故障诊断。采用总体平均经验模态分解(EEMD)方法来处理滚动轴承的振动信号,分解后可以得到很多内禀模态函数(IMF)。再把IMF能量作为特征向量输入到QPSA-RVM诊断器中对滚动轴承的故障进行准确诊断。实验结果显示:该模型可以更快地实现对滚动轴承故障的准确识别,证明了该模型的稳定性及高效性。与支持向量机(SVM)分析对比后,进一步体现出RVM方法在智能故障诊断领域中具有优势。
Human factors have great influence on the traditional methods of rolling bearing fault diagnosis,and the cause of the fault is complicated.To solve the problem,we put forward fault diagnosis method based on QPSO to optimize the relevance vector machine(RVM).It was applied to the rolling bearing fault diagnosis.The ensemble empirical mode decomposition(EEMD)was adopted to deal withvibration signal of rolling bearing.Several intrinsic mode functions(IMF)could be obtained after decomposition.IMF energy was input into QPSA-RVM diagnoser as feature vector,and the fault of rolling bearing was diagnosed accurately.The experimental results show that the model can quickly and accurately recognize the rolling bearing fault,and is proven to be stable and efficient.Through the analysis and contrast with SVM,the RVM method has more advantages in the field of intelligent fault diagnosis.
作者
吕维宗
王海瑞
舒捷
Lü Weizong;Wang Hairui;Shu Jie(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,Yunnan,China)
出处
《计算机应用与软件》
北大核心
2019年第1期6-11,16,共7页
Computer Applications and Software
基金
国家自然科学基金项目(61263023)
关键词
量子粒子群算法
故障诊断
相关向量机
EEMD
Quantum-behaved particle swarm optimization
Fault diagnosis
Relevance vector machine
EEMD
