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| A Study on the Detection of Wear Particle Content of Lubricating Oil Based on Reflectance Spectrum |
| LIU Xue-jing1, CUI Hong-shuai1, YIN Xiong1, MA Shi-yi1, ZHOU Yan1*, CHONG Dao-tong1, XIONG Bing2, LI Kun2 |
1. School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China
2. AECC Sichuan Gas Turbine Establishment, Chengdu 610500, China
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Abstract When wear occurs between the internal transmission components of the engine, fine metal wear particles will fall off between the internal components of the equipment, which will seriously affect the normal operation of the engine and even cause serious accidents. Therefore, it is necessary to monitor the information of wear particles in lubricating oil online. In this paper, the detection experiment of lubricating oil wear particle content was carried out based on the quantitative analysis of the reflection spectrum. By building the experimental platform for detecting the wear particle content of lubricating oil by reflection spectrum, two kinds of Fe particles and Cu particles with particle sizes of 300 mesh (50 μm) fatigue wear particles and 80 mesh (175 μm) severe wear particles were selected. In the visible light band (450～760 nm) and the ultraviolet band (200～435 nm), 31 sets of reflection spectrum data of lubricating oil wear particle concentration in the range of 6～15 μg·mL-1 with an interval of 0.3 μg·mL-1 were obtained. Firstly, a partial least squares (PLS) linear model was established for the reflectance spectral data, but the prediction effect of the model was poor. Therefore, the data preprocessing correction method is used to screen and correct the original data. The interference factors in the modeling data are reduced, and the PLS optimization model is established. However, it is found that although the PLS optimization model improves the prediction effect, it is still poor under some working conditions. To further optimize the prediction effect of the model, a genetic programming model and a genetic programming-partial least squares (Genetic Programming-PLS) model are established. Finally, the following conclusions are drawn: the model determination coefficient R2 is in the range of 0.71～0.80 in the PLS linear model, 0.80～0.94 in the PLS optimization model, 0.72～0.96 in the genetic programming model, and 0.84～0.98 in the genetic program-PLS model. The results showed that the genetic programming-PLS model had the best prediction effect. The study of the reflectance spectroscopy of wear particle content in lubricating oil is expected to provide a new method for engine oil monitoring.
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Received: 2024-03-12
Accepted: 2024-07-11
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Corresponding Authors:
ZHOU Yan
E-mail: yan.zhou@mail.xjtu.edu.cn
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