CAJ | 학술논문

对轴承振动信号进行时频分析获得全特征集；运用距离补偿法提取轴承故障敏感特征获得敏感特征集。两种特征集在用于训练、测试轴承状态时不仅诊断率不同，且误判样本亦不同。基于此，提出基于集成隐马尔可夫模型的轴承故障诊断方法。采用两种特征集分别建立两独立隐马尔可夫模型；运用平均法则、最大似然概率法集成隐马尔可夫模型分类效果；对轴承信号进行故障诊断。实验结果表明，与基于敏感特征集、全特征集的分类器相比，该模型分类器在轴承故障诊断中识别精度更高。
대축승진동신호진행시빈분석획득전특정집；운용거리보상법제취축승고장민감특정획득민감특정집。량충특정집재용우훈련、측시축승상태시불부진단솔불동，차오판양본역불동。기우차，제출기우집성은마이가부모형적축승고장진단방법。채용량충특정집분별건립량독립은마이가부모형；운용평균법칙、최대사연개솔법집성은마이가부모형분류효과；대축승신호진행고장진단。실험결과표명，여기우민감특정집、전특정집적분류기상비，해모형분류기재축승고장진단중식별정도경고。
Full features of a bearing vibration signal in time and frequency domain were extracted at first.A compensation method based on distance was used to choose features sensitive to bearing faults.Then full features and sensitive features vectors were built.The results using hidden markov model (HMM)based on those two features were different.Then the method of integrated HMM for bearing fault diagnosis was proposed.Based on independent HMM classifiers trained with those two different feature vectors,the average rule and the maximum likelihood probability method were used to integrate the two HMM classifiers.The experimental results showed that the proposed method has a higher recognition rate compared with the two independent classifiers based on different feature vectors.