CAJ | 학술논문

采用声发射方法监测得到的复杂机械密封的声发射信号往往信噪比很低，对其工作状态进行分类存在一定的困难。提出一种基于声发射和小波神经网络的机械密封工作状态分类的方法。该方法将小波与神经网络结合，基于声发射信号时域和小波包能量分析的特征提取方法，充分利用声发射信号中的有用信息，能很好地表征机械密封的工作状态。以旋转轴用动密封装置为例，采用上述方法对其工作状态进行监测。实验证明，该方法能够有效地对复杂机械密封的工作状态或故障类型进行分类。
채용성발사방법감측득도적복잡궤계밀봉적성발사신호왕왕신조비흔저，대기공작상태진행분류존재일정적곤난。제출일충기우성발사화소파신경망락적궤계밀봉공작상태분류적방법。해방법장소파여신경망락결합，기우성발사신호시역화소파포능량분석적특정제취방법，충분이용성발사신호중적유용신식，능흔호지표정궤계밀봉적공작상태。이선전축용동밀봉장치위례，채용상술방법대기공작상태진행감측。실험증명，해방법능구유효지대복잡궤계밀봉적공작상태혹고장류형진행분류。
The signal acquired by acoustic emission method to monitoring the complex mechanical seals usually has a low SNR signal,which makes the classification of the working condition of mechanical seals difficult.A new method was presented to classify the condition of mechanical seals based on acoustic emission and wavelet neural network.This method was combined of the wavelet and neural network,and extracted characteristics based on time domain and wavelet pack a-nalysis to make full use of the useful information in acoustic emission signal,which could represent the working condition of mechanical seals well.With an experiment on the dynamic seals for rotating shaft,the method mentioned above was used to monitor the working condition.The experiment proves that this method can classify the working condition and fault pattern of complex mechanical seals effectively.