摘要
研究了基于发动机滑油滤磨屑图像的磨损状态自动识别技术。首先采用最大熵法和数学形态学方法,提取滑油滤磨屑图像中反映磨损状态的特征量;然后采集反映正常状态的航空发动机滑油滤图像,通过图像分析与特征提取,构造出仅包含正常样本的训练样本集,最后用野点检测方法对训练样本进行学习,并使用遗传算法对野点检测参数进行优化,得到了滑油滤磨屑图像的正常域,并以此来识别航空发动机磨损状态的严重程度。开发了发动机滑油滤监控系统(engine oil filter monitoring system,EOFMS),实现了基于野点检测的磨屑图像识别功能,并利用实际航空发动机滑油滤磨屑图像进行了实验分析,结果验证了该方法的有效性。
An automatic identification technology based on engine oil filter debris image was inves- tigated. Firstly, characteristic quantities of oil filter images which can reflect wear state were extracted with the maximum entropy method and mathematical morphology method. Then, oil filter images that can reflect normal state were collected, and the normal training sample sets were constructed through image analysis and feature extraction. Finally, the normal domain of oil filter image was ob- tained through normal training samples using novelty detection, and to identify the severity of the wear state of aero--engine according to the normal domain. Besides, the self adaptive parameter of novelty detection was obtained by genetic algorithm. Engine oil filter monitoring system （EOFMS） was developed, and the recognition function of wear debris images based on novelty detection was realized. Besides, the experiments using actual aero--engine oil filter debris images were carried out, and the results show the effectiveness of the method.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2010年第7期827-831,共5页
China Mechanical Engineering
关键词
航空发动机
磨损监测
滑油滤分析
图像分析
野点检测
遗传算法
aero-engine
wear monitoring
oil filter analysis
image analysis
novelty detection
genetic algorithm
