CAJ | 학술논문

为充分利用时域、频域以及时频域中的有效特征，提高滚动轴承故障诊断准确率，提出一种混合域特征集构建方法，利用原始信号分别生成时域和频域特征集，通过经验模式分解提取固有模态函数的排列熵和 Hilbert 谱的奇异值作为时频域特征集，使得混合域特征集比单域特征更能全面准确反映轴承运行状态。针对混合域特征集存在维数过高、特征之间冗余性严重的问题，采用加权最大相关最小冗余的特征选择方法，以支持向量机分类正确率为依据，选取7个有效特征向量。实验结果表明：基于 WMRMR 的混合域特征选择方法的分类准确率可达98％，能够有效的识别轴承故障信息。
위충분이용시역、빈역이급시빈역중적유효특정，제고곤동축승고장진단준학솔，제출일충혼합역특정집구건방법，이용원시신호분별생성시역화빈역특정집，통과경험모식분해제취고유모태함수적배렬적화 Hilbert 보적기이치작위시빈역특정집，사득혼합역특정집비단역특정경능전면준학반영축승운행상태。침대혼합역특정집존재유수과고、특정지간용여성엄중적문제，채용가권최대상관최소용여적특정선택방법，이지지향량궤분류정학솔위의거，선취7개유효특정향량。실험결과표명：기우 WMRMR 적혼합역특정선택방법적분류준학솔가체98％，능구유효적식별축승고장신식。
In order to improve the accuracy of rolling bearings fault diagnosis by making full use of effective features in time domain,frequency domain and time-frequency domain,a mixed domain feature construction approach was proposed.With it,time domain and frequency domain features were generated using the original signals,permutation entropies of intrinsic mode functions obtained with EMD and singular values of Hilbert spectrum were extracted as time-frequency domain feature sets,and mixed domain feature sets were made to more fully and accurately reflect bearing running states than the single domain features do.Aiming at mixed domain feature sets having shortcomings of too high dimensions and serious redundancy,a feature selection method based on weighted minimal redundancy maximal relevance (WMRMR)was proposed,it could select seven major feature vectors based on the classification accuracy of support vector machine.The test results showed that the classification accuracy of mixed domain feature selection can reach 98%based on WMRMR,and it can effectively identify the bearing fault information.