基于MRSVD和VPMCD的轴承故障智能诊断方法-计算机工程与应用.PDF

Computer Engineering and Applications 计算机工程与应用 2016 ，52（8） 153 基于MRSVD 和VPMCD 的轴承故障智能诊断方法研究 李 葵，范玉刚，吴建德 LI Kui, FAN Yugang, WU Jiande 1.昆明理工大学 信息工程与自动化学院，昆明 650500 2.云南省矿物管道输送工程技术研究中心，昆明 650500 1.Faculty of Information Engineering Automation, Kunming University of Science and Technology, Kunming 650500, China 2.Engineering Research Center for Mineral Pipeline Transportation. YN, Kunming 650500, China LI Kui, FAN Yugang, WU Jiande. Research on bearing fault intelligent diagnosis method based on MRSVD and VPMCD. Computer Engineering and Applications, 2016, 52 （8 ）：153-157. Abstract ：In view of the problem that the feature components are easy to be submerged in noise at early stage of bearing fault, and the difficulty to obtain a large number of fault samples in practice, a bearing fault intelligent diagnosis method based on Multi-Resolution Singular Value Decomposition （MRSVD ）and Variable Predictive Model based Class Discriminate （VPMCD ）is put forward. The detail components that contain the fault features are extracted by using the MRSVD for decomposing the bearing acceleration vibration signal. The logarithmic normal distribution models are established to high- light the non Gauss feature of detail components. The feature vector is constructed by calculating logarithmic means and logarithmic standard deviations. It is used to identify the fault by VPMCD. The method is applied to the actual bearing fault diagnosis with local weak faults of outer, inner circle and balls, and the fault type can be accurately identified with 98.75% accuracy. The result shows that the presented method is feasible and valid. Key words ：Multi-Resolution Singular Value Decomposition （MRSVD ）; Variable Predictive Model based Class Discrimi- nate （VPMCD ）; fault diagnosis 摘 要：针对轴承早期微弱故障特征信息易被噪声掩盖和现实中难以获得大量典型故障样本的实际情况，提出了基 于多分辨奇异值分解（Multi-Resolution Singular Value Decomposition ，