RESEARCH PAPER
Predicting motor oil condition using artificial neural networks and principal component analysis
| 1 | CISE, Univ. Beira Interior, Covilhã, 6201-001, Portugal |
| 2 | Industrial Eng. and Management, Univ. Lusófona, Campo Grande 376, 1749-024, Lisboa, Portugal |
| 3 | Polytechnic Institute of Coimbra – ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal |
| 4 | CEMMPRE, Coimbra University, DEM, Polo 2, 3030-290 Coimbra, Portugal |
| 5 | ISR, Coimbra University, DEEC, Polo 2, 3030-290 Coimbra, Portugal |
Publication date: 2020-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):440–448
KEYWORDS
ABSTRACT
The safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the
lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations
that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the
oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from
two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for
determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions,
while in other cases the oil passed the ideal change point.
REFERENCES (20)
1.
Capone S, Zuppa M, Presicce D S, Francioso L, Casino F, Siciliano P. Metal oxide gas sensor array for the detection of diesel fuel in engine oil. Sensors and Actuators B: Chemical 2008; 131(1): 125-133, https://doi.org/10.1016/j.snb.....
2.
Cerny B A, Kaiser H F. A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research 1977; 12(1): 43-47, https://doi.org/10.1207/s15327....
3.
Du L, Zhe J. As high throughput inductive pulse sensor for online oil debris monitoring. Tribology International 2011; 44(2): 175-179, https://doi.org/10.1016/j.trib....
4.
El-Hag A H, Saker Y A, Shurrab I Y. Online oil condition monitoring using a partial discharge signal. IEEE Transactions on Power Delivery 2010; 26(2): 1288-1289, https://doi.org/10.1109/TPWRD.....
5.
Gajewski J, Valis D. The determination of combustion engine condition and reliability using oil analysis by MLP and RBF neural networks. Tribology International 2017; 115: 557- 572, https://doi.org/10.1016/j.trib....
6.
Ghobadian B, Rahimi H, Nikbakht A, Najafi G, Yusaf T. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable energy 2009; 34(4): 976-982, https://doi.org/10.1016/j.rene....
7.
Hongxiang T, Yuntao L, Xiangjun W. Application of neural network to diesel engine SOA. Third International Conference on Measuring Technology and Mechatronics Automation 2011; IEEE, https://doi.org/10.1109/ICMTMA....
8.
Kumar S, Mukherjee P, Mishra N. Online condition monitoring of engine oil. Industrial lubrication and tribology 2005; 57(6): 260-267, https://doi.org/10.1108/003687....
9.
Li X, Li J, He D, Qu Y. Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (3): 403-410, https://doi.org/10.17531/ein.2....
10.
Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 63-72, https://doi.org/10.17531/ein.2....
11.
Niu X, Yang C, Wang H, Wang Y. Investigation of ann and svm based on limited samples for performance and emissions prediction of a crdiassisted marine diesel engine. Applied Thermal Engineering 2017; 111: 1353-1364, https://doi.org/10.1016/j.appl....
12.
Parlak A, Islamoglu Y, Yasar H, Egrisogut A. Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Applied Thermal Engineering 2006; 26(8-9): 824-828, https://doi.org/10.1016/j.appl....
13.
Raposo H, Farinha J T, Fonseca I, Galar D. Predicting condition based on oil analysis - a case study. Tribology International 2019; 135: 65-74, https://doi.org/10.1016/j.trib....
14.
Rodrigues J, Costa I, Farinha J T, Mendes M, Margalho L. Modelling diesel engine oil condition using artificial neural networks. eMaintenance 2019, Stockholm, Sweden.
15.
Shaban K, El-Hag A, Matveev A. A cascade of artificial neural networks to predict transformers oil parameters. IEEE Transactions on Dielectrics and Electrical Insulation 2009; 16(2): 516-523, https://doi.org/10.1109/TDEI.2....
16.
Westerholm R, Li H. A multivariate statistical analysis of fuel-related polycyclic aromatic hydrocarbon emissions from heavy-duty diesel vehicles. Environmental Science & Technology 1994; 28(5): 965-972, https://doi.org/10.1021/es0005....
17.
Yonghui Y, Weihua W, Xinpin Y, Hanliang X, Chengtao W. An integrated online oil analysis method for condition monitoring. Measurement Science and Technology 2003; 14(11): 1973-1977, https://doi.org/10.1088/0957-0....
18.
Zhu J, He D, Bechhoefer E. Survey of lubrication oil condition monitoring, diagnostics, and prognostics techniques and systems. Journal of Chemical Science and Technology 2013; 2(3): 100-115.
19.
Zhu J, Yoon J M, He D, Bechhoefer E. Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines. Wind Energy 2015; 18(6): 1131-1149, https://doi.org/10.1002/we.174....
20.
Zhu X, Zhong C, Zhe J. A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring. Measurement Science and Technology 2017; 28(7), https://doi.org/10.1088/1361-6....
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