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....
CITATIONS (18):
1.
Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition
João Rodrigues, José Farinha, Mateus Mendes, Ricardo Mateus, António Cardoso
Energies
João Rodrigues, José Farinha, Mateus Mendes, Ricardo Mateus, António Cardoso
Energies
2.
Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms
Tomasz Rymarczyk, Grzegorz Kłosowski, Anna Hoła, Jerzy Hoła, Jan Sikora, Paweł Tchórzewski, Łukasz Skowron
Energies
Tomasz Rymarczyk, Grzegorz Kłosowski, Anna Hoła, Jerzy Hoła, Jan Sikora, Paweł Tchórzewski, Łukasz Skowron
Energies
3.
Experimental and Theoretical Research on Pressure Drop Changes in a Two-Stage Air Filter Used in Tracked Vehicle Engine
Tadeusz Dziubak, Grzegorz Boruta
Separations
Tadeusz Dziubak, Grzegorz Boruta
Separations
4.
Predictive Maintenance Tools – A Global Survey
Joao Rodrigues, Farinha Torres, Cardoso Marques
WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
Joao Rodrigues, Farinha Torres, Cardoso Marques
WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
5.
Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning
João Rodrigues, Alexandre Martins, Mateus Mendes, José Farinha, Ricardo Mateus, Antonio Cardoso
Energies
João Rodrigues, Alexandre Martins, Mateus Mendes, José Farinha, Ricardo Mateus, Antonio Cardoso
Energies
6.
Design of a tribotechnical diagnostics model for determining the technical condition of an internal combustion engine during its life cycle
Jan Furch, Josef Jelínek
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Jan Furch, Josef Jelínek
Eksploatacja i Niezawodnosc - Maintenance and Reliability
7.
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study
Alexandre Martins, Inácio Fonseca, José Farinha, João Reis, António Cardoso
Applied Sciences
Alexandre Martins, Inácio Fonseca, José Farinha, João Reis, António Cardoso
Applied Sciences
8.
Anticipating Future Behavior of an Industrial Press Using LSTM Networks
Balduíno Mateus, Mateus Mendes, José Farinha, António Cardoso
Applied Sciences
Balduíno Mateus, Mateus Mendes, José Farinha, António Cardoso
Applied Sciences
9.
IC Engine Dynamic oil Life Prediction Using Machine Learning Approach
Santosh Jangamwadimath, Chirantan Gayakwad, Nagaraj Banapurmath, Ashwin Kubasadgoudar, Vishal Pattanashetty, Shashwat Suyash, Nalini Iyer, Shashidhar Shiva, Priyamvad Priyadarshi
SAE Technical Paper Series
Santosh Jangamwadimath, Chirantan Gayakwad, Nagaraj Banapurmath, Ashwin Kubasadgoudar, Vishal Pattanashetty, Shashwat Suyash, Nalini Iyer, Shashidhar Shiva, Priyamvad Priyadarshi
SAE Technical Paper Series
10.
Proceedings of IncoME-VI and TEPEN 2021
Balduíno Mateus, Mateus Mendes, José Farinha, Alexandre Martins, António Cardoso
Balduíno Mateus, Mateus Mendes, José Farinha, Alexandre Martins, António Cardoso
11.
Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach
Mohammad Rahimi, Mohammad-Reza Pourramezan, Abbas Rohani
Expert Systems with Applications
Mohammad Rahimi, Mohammad-Reza Pourramezan, Abbas Rohani
Expert Systems with Applications
12.
Internal combustion engine diagnostics using statistically processed Wiebe function
Jan Famfulik, Michal Richtar, Jakub Smiraus, Petra Muckova, Branislav Sarkan, Pavel Dresler
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Jan Famfulik, Michal Richtar, Jakub Smiraus, Petra Muckova, Branislav Sarkan, Pavel Dresler
Eksploatacja i Niezawodnosc - Maintenance and Reliability
13.
Forecasting Steel Production in the World—Assessments Based on Shallow and Deep Neural Networks
Balduíno Mateus, Mateus Mendes, José Farinha, António Cardoso, Rui Assis, Costa da
Applied Sciences
Balduíno Mateus, Mateus Mendes, José Farinha, António Cardoso, Rui Assis, Costa da
Applied Sciences
14.
Wind Farm and Resource Datasets: A Comprehensive Survey and Overview
Diogo Menezes, Mateus Mendes, Jorge Almeida, Torres Farinha
Energies
Diogo Menezes, Mateus Mendes, Jorge Almeida, Torres Farinha
Energies
15.
Analysis of the influence of hydraulic fluid quality on external gear pump performance
Borivoj Novaković, Ljiljana Radovanović, Ninoslav Zuber, Dragica Radosav, Luka Đorđević, Mila Kavalić
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Borivoj Novaković, Ljiljana Radovanović, Ninoslav Zuber, Dragica Radosav, Luka Đorđević, Mila Kavalić
Eksploatacja i Niezawodnosc - Maintenance and Reliability
16.
Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
Diogo Cardoso, Luís Ferreira
Applied Sciences
Diogo Cardoso, Luís Ferreira
Applied Sciences
17.
Review of fault detection techniques for predictive maintenance
D. Divya, Bhasi Marath, Kumar Santosh
Journal of Quality in Maintenance Engineering
D. Divya, Bhasi Marath, Kumar Santosh
Journal of Quality in Maintenance Engineering
18.
Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM
Zhongxin Liu, Huaiguang Wang, Mingxing Hao, Dinghai Wu
Lubricants
Zhongxin Liu, Huaiguang Wang, Mingxing Hao, Dinghai Wu
Lubricants
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.