一种基于免疫识别的滚动轴承故障检测模型  

Rolling Bearing Fault Detection Based on Immune Recognition

作　　者：侯胜利[1] 王威[1] 周根娜[1] 乔丽[1] 

机构地区：[1]徐州空军学院,江苏徐州221006

出　　处：《机床与液压》2009年第7期258-261,共4页Machine Tool & Hydraulics

摘　　要：提出了一种基于免疫识别的滚动轴承故障检测模型。该模型根据免疫识别原理来构造神经网络检测器,通过训练将滚动轴承的故障模式信息存储于分布的检测器中,检测器用于捕获被检测轴承的异常模式特征,当检测器与特征样本匹配时则激活该检测器,根据检测器的激活情况来发现轴承的故障,并给出了相应的训练算法。通过滚动轴承损伤检测的仿真实验,表明该方法对由轴承损伤冲击造成的信号突变保持了较高的灵敏度和分辨率,对于滚动轴承的监测具有一定的应用价值。A new model of rolling bearing fault detecting based on immune recognition was proposed. In this model, neural networks-based detectors were constructed based on the principle of immune recognition. Taking advantage of neural networks training, the information of bearing fault patterns was stored in the distributed neural networks-based detectors. These detectors were used to capture the anomalous pattern features of detected bearings. When a detector was matched up with a feature sample, the detector was stimulated. Through the relevant stimulated detectors a fault can be found out. The principle and structure of the model were presented, and its training algorithm was derived. The fault diagnosis scheme using this model was investigated. Defect detection simulations of rolling bearings shows that the proposed model has high resolution for locations of signal singularities, which are caused by impacts between defect and its mating surface in the bearing. This method is contributive for rolling bearing status monitoring.

关 键 词：滚动轴承 故障检测 免疫识别 人工神经网络 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]