降级(电信)
荧光光谱法
润滑
润滑性
工艺工程
光谱学
状态监测
基质(化学分析)
汽车工程
计算机科学
光学(聚焦)
环境科学
荧光
材料科学
机械工程
工程类
电气工程
复合材料
量子力学
物理
光学
电信
作者
Oleg Sosnovski,Pooja Suresh,Alex Dudelzak,Benjamin Green
摘要
Lubrication oil is a vital component of heavy rotating machinery defining the machine's health, operational safety and effectiveness. Recently, the focus has been on developing sensors that provide real-time/online monitoring of oil condition/lubricity. Industrial practices and standards for assessing oil condition involve various analytical methods. Most these techniques are unsuitable for online applications. The paper presents the results of studying degradation of antioxidant additives in machinery lubricants using Fluorescence Excitation-Emission Matrix (EEM) Spectroscopy and Machine Learning techniques. EEM Spectroscopy is capable of rapid and even standoff sensing; it is potentially applicable to real-time online monitoring.
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