摘要
基于磨粒分析的油液监测技术，通常都偏重于从形态学方面进行研究，以获取单颗磨粒的“个体”信息，很少开展磨粒整体统计学方面的“整体”信息研究．其实，研究磨粒“整体”信息具有诸多优点，关键是如何从“整体”信息中获取反映摩擦学系统磨损状态转变和磨损程度分级的特征．通过在８ＮＶＤ４８Ａ－２Ｕ型柴油机上的现场监测试验研究，利用铁谱分析的ＤＬ和ＤＳ值，以及光谱分析的元素浓度值所包含的磨粒“整体”信息，建立了柴油机磨损程度的基准线，构造了磨损趋势校正的灰色预测模型，并且利用预测值和实测值的漂移值，设计了磨损趋势预测值的区间估计方法，以用于预报未来磨损的等级．这种基于磨粒“整体”信息的磨损预测方法，已用于８ＮＶＤ４８Ａ－２Ｕ型柴油机的实际监测。
For oil monitoring techniques based on wear particle analysis, people lay over emphasis on studying the separate information about edge, surface texture and colour of a wear particle by morphology and neglect the research on statistics information of all wear particles. However, it is more easy to obtain the statistics information than to obtain the separate information and it is the key how to get characteristic parameters which represent changes of wear conditoin and classification of wear extent from the statistics information. During the monitoring experiment of 8NVD48A 2U diesel engine under running condition, the methods to describe wear generated in diesel engine are studied according to the wear particles statistics information obained from D L, D s value of spectrometric analysis. Firstly, baselines of wear extent are established. Secondly, the revised grey predictive model of the wear trendency is developed. Thirdly, the way of estimating interval of wear predictive value is designed with the deviation between predictive value and measurements, and the interval is used to determine wear classification in the future. the developed wear predictive models are applied to the monitoring experiment of 8NVD48A 2U diesel engine under running condition and the good results have been received.
出处
《摩擦学学报》
EI
CAS
CSCD
北大核心
1996年第4期358-366,共9页
Tribology
基金
武汉市晨光计划资助项目
关键词
磨损
预测
灰色理论
柴油机
wear particle wear predict grey theroy information fusion oil monitoring diesel engine
