基于油液降噪信息的发动机磨损多特征分析研究  

Study on Multi-feature Analysis of Engine Wear Based on Oil Denoised Data

作　　者：徐启圣[1] 许泽银[1] 徐厚昌[1] 

机构地区：[1]合肥学院,合肥230022

出　　处：《中国机械工程》2010年第12期1405-1409,共5页China Mechanical Engineering

基　　金：安徽省教育厅优秀青年人才基金资助项目(2009SQRZ160);合肥学院人才引进基金资助项目(600855)

摘　　要：由于难以确定合适的油液检测数据处理方法,应用油液分析监测发动机磨损状态的有效性和针对性不高。为此,根据经小波包降噪的光谱数据和直读铁谱数据,在用新三线值法提取边界特征及用K-means聚类法获取质心特征的基础上,提出了质心-边界多特征分析方法,以确定发动机主要摩擦副的磨损状态。结果表明,多特征分析法比传统三线值法更详细、准确,使磨损状态的识别率提高一倍。信号的相关特征研究表明,活塞环摩擦副的磨损出现了异常,进一步证实了多特征分析的准确性和有效性。To advance efficiency and pertinence in monitoring engine wear condition applying oil analysis,oil spectrometric and direct-reading ferrography data denoised by wavelet packet was used to extract three boundary features.In combination with centroids feature obtained through K-means clustering,centroid-boundary multi-feature analysis was established to monitor wear state of main friction pairs of certain engine,and the decision table about wear condition was formed.The result shows that compared to the decision table obtained by traditional three-line method,the partition of wear state based on multi-feature analysis is more detailed,accurate and instructionally significant.The identification efficiency for wear state increases by one time.Moreover,the study of the relativity between oil spectrometric signals indicates the abnormal wear of piston ring friction pairs,which further confirms the pertinence and availability of multi-feature analysis.

关 键 词：特征提取 边界特征 聚类特征 相关度 多特征分析 

分 类 号：TH117.1[机械工程—机械设计及理论]