摘要
针对滚动轴承故障诊断中出现的多故障分类问题,提出了一种利用自适应Morlet小波和小生境遗传算法(niche genetic algorithm,简称NGA)优化支持向量机(support vector machine,简称SVM)实现滚动轴承故障诊断的新方法。首先,采用自适应Morlet小波方法提取出最佳尺度附近的3个信号分量作为特征信号,分别计算它们的Shannon能量熵值作为特征量得到样本集,作为SVM的输入向量,并用样本集训练1-v-r SVM;然后,再构造一种新的核函数,并用NGA在SVM训练过程中对核函数参数进行优化,提高SVM学习机器的分类性能;最后,将本研究方法用于对含有较强噪声的实际滚动轴承的内圈、外圈、滚珠故障样本进行了分类识别。结果表明,该方法具有较好的抗噪和分类能力,验证了其有效性和可行性。
A rolling bearing fault diagnosis method is proposed based on adaptive Morlet wavelet and NGA optimized SVM.Firstly,three signal components nearby the appropriate scale as characteristic signals are extracted by adaptive Morlet wavelet,and their Shannon energy entropy are calculated respectively to form the sample set as input vector of SVM,in order to train the 1-v-r SVM.Then,a new nuclear function of SVM is constructed,and the kernel function parameters are optimized in the SVM training process by NGA in order to improve the classification performance of SVM.Finally,the experiment is carried out with the noisy rolling bearing mechanical fault data to prove its reliability and veracity.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2013年第5期751-755,908,共5页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51277166
51175478
51205369)
浙江省自然科学青年基金资助项目(LQ12E07002)
浙江省博士后科研择优资助项目(BSH1302015)
