摘要
针对由复杂非线性机床主要部件弱故障引起的与动态刚度不足相关的加工颤振较难量化判别溯源的问题,提出了一种基于PCA/D-S方法(PCA:主成分分析;D-S:证据理论)的故障部件粗判别,及基于FUKL融合算法(模糊集与相对熵融合)的细粒度辨识相结合的动刚度特征研究方法。该方法采集加工态的多部件振动特征,通过分离时频域特征值,利用PCA降维获得相关性强的低维特征,通过D-S计算合成证据概率,粗定位出故障部件,而后通过FUKL融合算法,进一步准确计算出故障辨识结果。将所提方法应用于实际颤振机床的故障溯源研究中,从4个主要部件中,以78.69%的合成证据概率判别出主轴系统存在动刚度不足,通过FUKL辨识出主轴系统的轴向动刚度不足的故障本质,通过拆解故障部件测试实际载荷,分析验证了所提算法运算结果的正确性。
Aiming at the problem of quantitatively identifying and tracing the source of machining chatter related to insufficient dynamic stiffness caused by weak faults of main components of complex nonlinear machine tools,a research method of dynamic stiffness characteristics based on rough identification of fault components based on PCA/D-S method(PCA:principal component analysis;D-S:evidence theory)and fine-grained identification based on FUKL fusion algorithm(fusion of fuzzy set and relative entropy)is proposed.In this method,the vibration characteristics of multi parts in the processing state are collected,and the time-frequency domain eigenvalues are separated,then the PCA is used to reduce the dimension to obtain the low-dimensional features with strong correlation.By calculating the synthetic evidence probability through D-S,the fault parts are roughly located,and then the fault identification results are further accurately calculated through FUKL fusion algorithm.The proposed method is applied to the fault traceability research of the actual chatter machine tool.From the four main components,the insufficient dynamic stiffness of the spindle system is identified with the synthetic evidence probability of 78.69%.The fault essence of the insufficient axial dynamic stiffness of the spindle system is identified by FUKL.The correctness of the operation results of the proposed algorithm is analyzed and verified by disassembling the faulty components and testing the actual load.
作者
王伟平
王琦
于洋
李宁
WANG Wei-ping;WANG Qi;YU Yang;LI Ning(School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China;Liaoning University of Technology,Jinzhou 121001,China;School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2022年第3期771-782,共12页
Journal of Vibration Engineering
基金
国家重点研发计划资助项目(2018YFB2000300)。
