摘要
机电设备油液渗漏是一种典型的多发性故障,利用人工嗅觉技术对渗漏油液挥发气体进行测试从而进行故障诊断是一种新的无损检测方法。使用人工嗅觉系统对模拟柴油、齿轮油和机油渗漏产生的挥发气体进行检测,结果为三维数据阵列(样本×时间×传感器)。应用二维主成分分析法和三维平铺主成分分析法、平行因子分析方法对三种油液挥发气体样本进行分类,结果表明平行因子分析法由于利用了数据集的三维结构信息,所以分类效果更佳;应用主成分回归方法实现了机油挥发气体样本的定量确定,说明使用人工嗅觉系统实现设定阈值报警是可能的。
Oil leakage from cracks is a kind of common fault in mechatronics systems. Responses of artificial olfactory system to the volatile organic compounds （VOCs） emitted by leakage oil can be used for leak detection, which is a novel non-destructive method for fault diagnosis. An artificial olfactory system was applied to detect three different kinds of leakage oil such as diesel oil, machine oil and gear oil. The collected dataset was arranged in a three-dimensionality matrix （ sample x time ~ sensor）. Three methods as two-way principal component analysis （ PCA ）, three- way unfolding PCA and parallel factor analysis （ PARAFAC ） were adopted to distinguish the VOCs. The results showed that PARAFAC was superior to the other two methods because PARAFAC took into account the true three-dimensionality structure of the dataset. The multivariate calibration method, principal component regression （PCR） was applied in the prediction of different concentrations of diesel oil. The results indicate that it is possible to use an artificial olfactory system to give an alarm by setting a threshold.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2012年第6期175-180,共6页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(50975279)
关键词
人工嗅觉系统
油液渗漏
主成分分析
平行因子分析
主成分回归
artificial olfactory system
oil leakage
principal component analysis （PCA）
parallel factor analysis （PARAFAC）
principal component regression （PCR）
