【作者】 孙芳； 柳亦兵； 李明； 赵凌波；

【Author】 Sun Fang, Liu Yibing, Li Ming, Zhao Lingbo Department of Automation, North China Electric Power University, Beijing 102206 IFS UFSOFT CO LTD China Electric Power Research Institute

【机构】 华北电力大学自动化系； 用友IFS公司； 中国电力科学研究院；

【摘要】 本文探讨了一种基于小波包和模糊模式识别理论的齿轮故障诊断方法。利用db3小波包对实际测量的齿轮振动信号进行4尺度分解分析,然后通过提取特征值(小波包系数方差),确定标准模糊子集和待识别模糊子集,利用模糊理论中的贴近度原则对于齿轮故障的不同阶段进行模糊分类,效果明显。文中还对信号作小波包分析和未作小波包分析后对测试信号分类的效果进行了比较,结果是小波包分析后再对测试信号进行模糊模式识别的准确率明显提高。更多还原

【Abstract】 The combined Wavelet Packet and Fuzzy Pattern Recognition method was used to diagnose the gear faults. Firstly, we use wavelet packet db3 (Daubechies3) by level 4 to decompose and analyze the measured vibration signals from a test gearbox. Secondly, the energy of wavelet packet coefficients was calculated as the characteristic values, by means of which the target fuzzy samples and testing fuzzy samples was set up. The fault condition of the gear was classified and identified with the testing fuzzy samples. A compare between being done and without being done wavelet packet analysis before the method of the fuzzy pattern recognition was also carried on. Results show that the combined using of wavelet packet analysis and fuzzy pattern recognition on faults diagnosis with vibration signals can gain good effects to search and read papers conveniently.更多还原