基于MSET和SPRT的内燃机气阀机构振动监测  

Vibration monitoring for valve drain of internal combustion engine based on MSET and SPRT

作　　者：姚良[1] 李艾华[1] 孙红辉[1] 张振仁[1] 

机构地区：[1]第二炮兵工程学院五系,陕西西安710025

出　　处：《振动工程学报》2009年第2期150-155,共6页Journal of Vibration Engineering

摘　　要：提出了一种基于多元状态估计(Multivariate State Estimation Techniques,MSET)和序贯概率比检验(Sequential Probability Ratio Test,SPRT)的内燃机气阀机构振动监测方法。在该方法中,首先建立正常工况下各监测参数之间的关联模型;然后根据系统当前观测特征向量与各建模样本特征向量之间的相似性程度,使用MSET对当前观测特征向量进行估计,得到与观测特征向量相对应的估计残差;最后使用SPRT对观测特征向量的估计残差进行均值和方差检验,确定系统的工作状态。试验中,通过设置不同的气阀间隙大小来模拟内燃机气阀机构不同程度的异常工况,以整周期缸盖振动信号幅值域特征作为系统工况监测参数。试验结果表明,MSET可有效增强故障状态下的信号特征呈现,而SPRT可在较少的周期内实现内燃机气阀机构异常工况的识别,MSET和SPRT的结合有效地实现了对内燃机气阀机构异常工况的早期监测。A novel approach for abnormal work condition recognition of internal combustion engine valve drain by monitoring vibration is proposed based on multivariate state estimation techniques （MSET） and sequential probability ratio test （SPRT）. In the approach, correlation model among monitoring parameters in normal work condition is constructed firstly. Then, according to the similarities between the current observed feature vector and each history feature vector contained in process memory matrix, estimation of the current feature vector is calculated by using MSET, and residual signal between the current feature vector and its estimation is obtained in turn. Finally, mean test and variance test for the residual signal is executed by using SPRT, and work condition of the system is pronounced. In the experiments, different abnormal work conditions are simulated by different valve gap settings, and temporal features of cylinder lip vibration signal in whole cycles are chosen to use as system monitoring parameters. Results demonstrated that MSET can effectively enhance the visualizing of the various fault characteristics, while the SPRT can perceive minute abnormal disturbance in a few whole cycle periods, thus the early alert for the abnormal work condition of the valve drain is achieved by the combining MSET and SPRT.

关 键 词：内燃机 气阀机构 振动监测 多元状态估计 序贯概率比检验 

分 类 号：TK413.43[动力工程及工程热物理—动力机械及工程] TP277[自动化与计算机技术—检测技术与自动化装置]