摘要
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。
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.
出处
《振动与冲击》
EI
CSCD
北大核心
2014年第10期92-96,共5页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(51175329)
