@inproceedings{3d9c22c3d30047aba3df06f15468b92d,
title = "Research on On-line Detection Technology of Lubricating Oil Moisture",
abstract = "Lubricating oil in mechanical equipment plays the functions of lubricating parts, removing impurities, heat conduction and cooling, sealing and rust prevention, load transmission and shock absorption, and is the guarantee for the safe and reliable operation of mechanical equipment. As the most destructive pollutant in lubricating oil, water pollution is the main reason for the deterioration of lubricating oil performance, which seriously threatens the service life of machinery and equipment. In view of the problems existing in the current lubricating oil moisture detection methods, this paper proposes an on-line lubricating oil moisture detection technology method, which uses the principle of image distortion caused by moisture media, senses the lubricating oil image through image sensing technology, and analyzes the water pollution degree, so as to realize the on-line detection of lubricating oil moisture.",
keywords = "Image distortion, Lubricating oil, Online detection, Water pollution",
author = "Bin Fan and Yingjie Gao and Lianfu Wang and Yong Liu and Jianguo Wang and Chao Zhang and Fengshou Gu",
note = "Funding Information: Acknowledgements. This work was supported in part by the National Natural Science Foundation of China under Grant (51965054,51865045), the Inner Mongolia Natural Science Foundation (No. 2021MS05041), the Inner Mongolia Autonomous Region Military-civilian Integration Key Scientific Research Projects and Soft Scientific Research Projects (JMZD202202), and in part by the State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System under Grant GZ2022KF004. Funding Information: Fig. 3. Reference ellipse shape parameters construction 4 Experimental Results This experiment was carried out at standard room temperature and the same humidity. Figure 4 shows the system schematic diagram and actual operation environment of the simulation experiment of lubricating oil moisture online detection. The specific experimental operation steps are as follows: (1) After fully shaking the oil sample, use the ultrasonic cleaner Branson 200 again to vibrate and defoaming all the oil samples. (2) Use a new syringe with a range of 1–20 ml to take 10 ml of oil sample with a water content of 0.1wt%, slowly inject it into the detection system with the syringe, and take its joint optical image in the upper computer. Except for the first 1 ml, take photos every 3 ml, measure three groups of optical image data respectively, and save them. (3) Change a new syringe, take 20 ml of fresh lubricating oil, slowly inject it into the detection system, and flush the flow channel. In order to ensure the reliability of the experimental data, repeat step 2, measure 3 groups of optical image data again, and a total of 6 groups of data are obtained. Take 20 ml fresh lubricating oil again to flush the flow passage. (4) Repeat experimental steps 2 and 3, and measure the experimental data of oil samples with water content of 0.2wt%, 0.3wt%, 0.4% wt, 0.5wt%, 0.6wt%, 0.7wt%, 0.8wt%, 0.9wt% and 1.0wt% respectively. CMOS Fixed structural object Runner body USB Focus lens LED light source Upper computer Homogenizing film Beaker Syringe Fig. 4. (a) Schematic diagram of detection system; (b) Actual experimental operation environment In this experiment, a total of 11 groups of optical joint distortion object image data of oil samples with water content of 0wt%, 0.1wt%, 0.2wt%, 0.3wt%, 0.4wt%, 0.5wt%, 0.6wt%, 0.7wt%, 0.8wt%, 0.9wt% and 1.0wt% were obtained. As shown in Fig. 5, six repeated experiments were carried out for each oil sample, and a total of 66 groups of experimental data were obtained. Rd180 parameters were extracted from the experimental images and their mean values were taken. The experimental results show that when the water content of the oil sample reaches 0.5wt%, the index extracted from the distorted object image will appear change points, as shown in Fig. 6. In the range ➀, that is, before the water content is 0.5wt%, it is a high growth range, and each index is significantly positively correlated with the water content of lubricating oil; In interval ➁, that is, after the water content is 0.5wt%, the growth trend of each index slows down, and saturation nonlinearity appears. Fig. 5. Distorted object images with different water content of lubricating oil Fig. 6. Variation curve of radial deviation RD180 In order to test the reliability of the optical sensing system, the WD-1 moisture sensor produced by Beijing Gepu company is used for comparative experiments. The experimental data are shown in Table 1. The data in the above table shows that the average error of WD-1 moisture sensor is very large, 35.22%, when the moisture content is very small, which is 0.3wt%, but with the increase of moisture content, its average error gradually decreases, which is in line with its test accuracy of ±0.1% (0–2%). The test accuracy of the optical sensor device Table 1. Comparison of detection results of two methods on different oil samples. Water Detection content device Optical sensing device WD-1 moisture sensor 0.3wt% Measured 0.27 0.30 0.28 0.22 value /wt% Error value /% Average error /% 10.0 0 5.57 6.7 26.0 35.22 0.5wt% Measured 0.54 0.56 0.54 0.47 value /wt% Error value /% Average error /% 8.0 9.33 12.0 8.0 6.0 12.6 0.7wt% Measured 0.71 0.74 0.79 0.59 value /wt% Error value /% Average error /% 1.4 6.63 5.7 12.8 15.7 0.17 0.19 43.0 36.67 0.57 0.59 14.0 18.0 0.60 0.66 14.3 5.7 11.9 developed by using the proposed optical sensing method in this study is also 0.1%, but its average error has been significantly lower than that of WD-1 moisture sensor, It can prove the reliability of the online detection sensor of lubricating oil moisture. Acknowledgements. This work was supported in part by the National Natural Science Foundation of China under Grant (51965054,51865045), the Inner Mongolia Natural Science Foundation (No. 2021MS05041), the Inner Mongolia Autonomous Region Military-civilian Integration Key Scientific Research Projects and Soft Scientific Research Projects (JMZD202202), and in part by the State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System under Grant GZ2022KF004. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference of The Efficiency and Performance Engineering Network 2022, TEPEN 2022 ; Conference date: 18-08-2022 Through 21-08-2022",
year = "2023",
month = mar,
day = "4",
doi = "10.1007/978-3-031-26193-0_51",
language = "English",
isbn = "9783031261923",
volume = "129",
series = "Mechanisms and Machine Science",
publisher = "Springer, Cham",
pages = "568--575",
editor = "Hao Zhang and Yongjian Ji and Tongtong Liu and Xiuquan Sun and Ball, {Andrew David}",
booktitle = "Proceedings of TEPEN 2022 -",
address = "Switzerland",
url = "https://tepen.net/, https://tepen.net/conference/tepen2022/",
}
