Fuzzy Measurement Based Image Testing for Oil Particles Contamination Level

被引:0
|
作者
Zhang, Yong [1 ]
Zhang, Xianming [1 ]
Li, Chuan [1 ]
Chen, Bin [1 ]
机构
[1] Chongqing Technol & Business Univ, Engn Res Ctr Oil Recovery Technol & Equipment, Minist Educ, Chongqing 400067, Peoples R China
关键词
Oil contamination level; oil particle imaging; particle target recognization; fuzzy measurement;
D O I
10.1117/12.870983
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The oil contamination level testing is important for its using and maintenance which is the basement of the oil contamination control is required higher by the developing device system, and the testing method is urgently needed to be studied for improving the process method and the maintenance quality of the contaminated oil. To classify the level of particles contamination in lubricant, CCD imaging technology is used to capture microscopic digital image of the oil particles sample. The digital image was processed and segmented in order that the computer can recognize and understand the particle targets by using image testing algorithm to measure the sizes, amounts and distributions of particles. The oil contamination level can be measured effectively by the economical and convenient method in which there is little air bubble and bead leading to false particle targets. To improve the influence produced by the false particle targets, One method is that a series of dynamical image samples from the contaminated oil in the multi-period and the multi-state are captured and used to test the particle targets, and the further method is to employ the fuzzy measurement using Gaussian subjection function, which describes the distribution of the standard evidences and the distribution of the testing data, and the testing probabilities of the evidence are weighed by the matching degree of the two distributions, which is used to classify the oil particles contamination level. The test shows that the oil particles contamination level diagnosis reliability is improved and the diagnosis uncertainty is reduced. This method combining with other testing methods by using the multi-information fusion method will be further studied later.
引用
收藏
页数:5
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