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研究滚动轴承故障诊断的有效方法,目前主要有神经网络、专家系统方法、模糊数学方法等,但是利用这些技术对滚动轴承进行故障诊断,由于获得的故障断数据存在不精确和不完备的缺陷,无法获得满意的诊断效果。为了能够弥补这一缺陷,将阶次小波包理论和变精度粗糙集理论结合起来对滚动轴承进行了故障诊断。仿真结果表明改进的方法故障诊断精度均达到了100%,从而表明了该方法具有较高的故障诊断精度,在滚动轴承的故障诊断中具有非常重要的应用价值。
At present, there are mainly neural networks, expert system methods and fuzzy mathematics methods to study the effective methods of fault diagnosis of rolling bearings. However, the rolling bearings are diagnosed using these techniques. Because of the imprecise and incomplete defects of the obtained fault data, Satisfactory diagnostic results. In order to make up for this defect, the rolling bearing is diagnosed by combining the order wavelet packet theory and the variable precision rough set theory. The simulation results show that the accuracy of the improved method of fault diagnosis reaches 100%, which shows that the method has high fault diagnosis accuracy and has very important application value in the fault diagnosis of the rolling bearing.