CAJ | 학술논문

　　针对轴承不同状态下的复杂性特征，提出基于排列熵和神经网络的异常检测与诊断方法。介绍异常检测与诊断的原理，采用排列熵算法检测信号异常，提取能够敏感反映轴承不同异常模式（滚动体异常、内圈异常和外圈异常）的排列熵、嵌入维数及关联维数等复杂度参数形成特征向量，通过神经网络对异常模式进行分类识别。结果表明：排列熵算法可以定位异常发生的时刻，以时间序列的复杂性特征参数为输入的神经网络诊断方法能够有效识别轴承的不同异常模式。
　　침대축승불동상태하적복잡성특정，제출기우배렬적화신경망락적이상검측여진단방법。개소이상검측여진단적원리，채용배렬적산법검측신호이상，제취능구민감반영축승불동이상모식（곤동체이상、내권이상화외권이상）적배렬적、감입유수급관련유수등복잡도삼수형성특정향량，통과신경망락대이상모식진행분류식별。결과표명：배렬적산법가이정위이상발생적시각，이시간서렬적복잡성특정삼수위수입적신경망락진단방법능구유효식별축승적불동이상모식。
Aiming at the complexity characteristics in different working conditions of rolling bearings, the method of abnormality detection and diagnosis based on permutation entropy and neural network was put forward. Principle of the abnormality detection and diagnosis was introduced, and the permutation entropy algorithm was used to detect signal abnormality. Then the eigenvector that was formed by complexity parameters of different abnormality models (boll, inner and outer abnormality of rolling bearing) was determined. Finally, abnormality diagnosis was carried out by neural network. Result shows that the permutation entropy algorithm can determine when the abnormality happens, and the diagnosis method based on neural network with the characteristics complexity parameters of time series as the input can effectively identify the different abnormality models.