基于EMD奇异值分解与马氏距离的气阀故障诊断  

Fault diagnosis for valve train based on EMD, SVD and Mahalanobis distance

作　　者：王旭平[1] 王汉功[1] 陈小虎[1] 

机构地区：[1]第二炮兵工程学院,陕西西安710025

出　　处：《机械》2008年第10期63-65,69,共4页Machinery

摘　　要：设置排气阀的不同间隙及用新气阀模拟轻微漏气,构造气阀机构的四种常见工作状态,然后针对非平稳的缸盖振动信号,介绍了一种可以处理非平稳信号的新方法,应用Hilbert-Huang变换的核心内容——经验模态分解法对非平稳信号进行分解,以降低原始信号中的非平稳性。利用经验模态分解和奇异值分解得到缸盖振动信号的故障特征参数,然后用少量的样本数据训练得到四种常见工作状态的模式向量,最后利用马氏距离判别函数进行气阀机构故障状态的识别。实验结果表明通过小样本即可完成模型的训练,且训练一旦完成,对未知样本的分类速度和识别率都很高,便于实现气阀机构故障的在线实时监测与诊断。By setting the different exhaust valve clearance and simulated minor leak with new valves, the four common working conditions of the valve train have been constructed. And against the non-stationary cylinder head vibration signals, a new method to improve non-stationary signals is introduced. The non-stationary signal is decomposed by empirical mode decomposition in Hilbert-Huang transform to reduce the non-stationarity in the signals. Based on this method and Singular Value Decomposition, some parameters extracted from cylinder head vibration signals are used for diagnosis. And then four common patterns vectors are received from small amount of training samples. Finally, MAHALANOBIS distance is used to identify the fault of the valve train. The results show that the model could finished with small amount of training samples, and once the training completed, the speed and recognition rate of unknown samples were both high. And it is easy to realize the on-line monitoring and diagnosis of valve train faults.

关 键 词：气阀机构 故障诊断 经验模态分解 奇异值分解 马氏距离 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]