Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM
Abstract
:1. Introduction
2. Materials and Methods
2.1. Oil Samples
2.2. AES Measurements
3. Parameter Selection
3.1. Information Entropy
3.2. Correlation Analysis
3.3. Potential Source of The Main Elements in Used Lubricating Oil
3.4. Feature Selection
- The average mass fraction of V and Ba elements is less than 1 μg·g−1, which has little effect on the quality change of lubricating oil;
- The information entropic value of oil spectrum data of five elements Al, V, B, Ba, and Mo is less than 2.5 bits, so these five elements cannot reflect the deterioration process of engine oil very well;
- The five elements Al, V, B, Ba, and Mo can be reflected by other elements with very strong correlation or strong correlation;
- The 12 elements Fe, Cu, Cr, Ni, Zn, P, K, Mg, Ca, Na, Si, and Pb can completely reflect the changes of wear particles, additives, and pollutants in lubricating oil.
4. RUL Prediction of Lubricating Oil Based on SVM
4.1. Principle of SVM
4.2. Lubricating Oil RUL Prediction Model
5. Results and Discussions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Description/Value |
---|---|
Engine type | Diesel |
Engine cylinder | V-8 |
Engine cooling mode | Water-cooling |
Element | 0 h | 10 h | … | 190 h | 200 h |
---|---|---|---|---|---|
Fe | 12.8 | 5.5 | … | 69.8 | 71.5 |
Al | 8.9 | 2.9 | … | 10.5 | 11.3 |
Cu | 5.2 | 2.1 | … | 7 | 7.3 |
Cr | 1.2 | 0.9 | … | 4.8 | 4.7 |
Ni | 2.9 | 2.8 | … | 1.5 | 3.2 |
V | 0 | 0.1 | … | 0.1 | 0 |
Zn | 1076 | 944 | … | 962 | 940 |
P | 1074 | 960 | … | 933 | 853 |
B | 6.1 | 27.7 | … | 4.6 | 3.8 |
Ba | 0.7 | 0 | … | 0.6 | 0.5 |
K | 57 | 3396 | … | 1111 | 1567 |
Mg | 39,818 | 38,374 | … | 34,071 | 34,149 |
Ca | 2830 | 2646 | … | 2915 | 2794 |
Na | 29.2 | 3.1 | … | 97.7 | 98.3 |
Si | 8.6 | 9 | … | 14 | 13.5 |
Mo | 0 | 3.1 | … | 0 | 0 |
Pb | 0 | 0.1 | … | 0 | 0.3 |
Category | Element | Symbol | Potential Source of the Specified Element |
---|---|---|---|
Abrasive Metals | Iron | Fe | Wear and rust of almost all parts (pistons, bearings, gears, crankshafts, shafts, rings, valves, etc.) |
Aluminum | Al | Wear of piston, bearing, and pump | |
Copper | Cu | Wear of connecting rod bushing and camshaft bushing, corrosion of radiator piping | |
Chrome | Cr | Wear of piston ring, cylinder sleeve, bearing, and the other alloy parts | |
Nickel | Ni | Wear of bearing, shaft, and gear | |
Vanadium | V | Wear of the alloy parts | |
Additive Elements | Zinc | Zn | Zinc dialkyl dithiophosphate (ZDDP) |
Phosphorus | P | Phosphate ester type base oil, antirust additive, load-carrying additives, friction modifier, and ZDDP | |
Boron | B | Load-carrying additives, friction modifiers, and purification dispersants | |
Barium | Ba | Purification dispersant, rust inhibitor, and demulsifying compound | |
Comprehensive | Potassium | K | Water pollution and coolant contamination |
Magnesium | Mg | Lubricant additives (purification dispersant, rust inhibitor), engine cylinder, water pollution, and coolant contamination | |
Calcium | Ca | Lubricant additives (purification dispersant), water pollution, and coolant contamination | |
Sodium | Na | Lubricant additives (rust inhibitor), water pollution, and coolant contamination | |
Silicon | Si | Lubricant additives (antifoaming agent), dust intrusion (poor working air filter), and piston wear | |
Molybdenum | Mo | Lubricant additives (load-carrying additives, friction modifier, and rust inhibitor) and ring wear | |
Lead | Pb | Fuel intrusion and white alloy bearing wear |
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Liu, Z.; Wang, H.; Hao, M.; Wu, D. Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM. Lubricants 2023, 11, 121. https://doi.org/10.3390/lubricants11030121
Liu Z, Wang H, Hao M, Wu D. Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM. Lubricants. 2023; 11(3):121. https://doi.org/10.3390/lubricants11030121
Chicago/Turabian StyleLiu, Zhongxin, Huaiguang Wang, Mingxing Hao, and Dinghai Wu. 2023. "Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM" Lubricants 11, no. 3: 121. https://doi.org/10.3390/lubricants11030121