PREDICTING MOTOR OIL CONDITION USING ARTIFICIAL NEURAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS

被引：23

作者：

Rodrigues, Joao ^{[1
,2
]}

Costa, Ines ^{[3
]}

Farinha, J. Torres ^{[3
,4
]}

Mendes, Mateus ^{[3
,5
]}

Margalho, Luis ^{[3
]}

机构：

[1] Univ Beira Interior, CISE, P-6201001 Covilha, Portugal

[2] Univ Lusofona, Ind Eng & Management, Campo Grande 376, P-1749024 Lisbon, Portugal

[3] Polytech Inst Coimbra ISEC, P-3030199 Coimbra, Portugal

[4] Coimbra Univ, CEMMPRE, DEM, Polo 2, P-3030290 Coimbra, Portugal

[5] Coimbra Univ, ISR, DEEC, Polo 2, P-3030290 Coimbra, Portugal

来源：

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY | 2020年 / 22卷 / 03期

关键词：

condition monitoring; oil analysis; multivariate analysis; predictive maintenance; FAULT-DIAGNOSIS; FUEL;

D O I：

10.17531/ein.2020.3.6

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：440 / 448

页数：9

共 50 条

[1] PREDICTING Ms TEMPERATURE APPLYING PRINCIPAL COMPONENT ANALYSIS-ARTIFICIAL NEURAL NETWORKS
Xu, Xuexia
Bai, Bingzhe
You, Wei
[J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2009, 23 (6-7): : 1099 - 1104
[2] Performance of neural networks for predicting yarn properties using principal component analysis
Chattopadhyay, R
Guha, A
Jayadeva
[J]. JOURNAL OF APPLIED POLYMER SCIENCE, 2004, 91 (03) : 1746 - 1751
[3] Stellar spectral classification using principal component analysis and artificial neural networks
Singh, HP
Gulati, RK
Gupta, R
[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 1998, 295 (02) : 312 - 318
[4] Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks
Malfatti, Luciano Heusser
Zampar, Aline
Galvao, Alessandro Cazonatto
Robazza, Weber da Silva
Boiago, Marcel Manente
[J]. LWT-FOOD SCIENCE AND TECHNOLOGY, 2021, 148
[5] Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis
Fakhri Yousefi
Hajir Karimi
Somayeh Mohammadiyan
[J]. Heat and Mass Transfer, 2016, 52 : 2345 - 2355
[6] Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis
Yousefi, Fakhri
Karimi, Hajir
Mohammadiyan, Somayeh
[J]. HEAT AND MASS TRANSFER, 2016, 52 (11) : 2345 - 2355
[7] Principal component analysis using constructive neural networks
Makki, B.
Seyedsalehi, S. A.
Hosseini, M. Noori
Sadati, N.
[J]. 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, 2007, : 558 - +
[8] Predicting octane numbers relying on principal component analysis and artificial neural network
Tipler, S.
D'Alessio, G.
Van Haute, Q.
Parente, A.
Contino, F.
Coussement, A.
[J]. COMPUTERS & CHEMICAL ENGINEERING, 2022, 161
[9] Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique
Boukhatem, B.
Kenai, S.
Hamou, A. T.
Ziou, Dj.
Ghrici, M.
[J]. COMPUTERS AND CONCRETE, 2012, 10 (06): : 557 - 573
[10] Improving principal component analysis (PCA) in automotive body assembly using artificial neural networks
Jang, KY
Yang, K
[J]. JOURNAL OF MANUFACTURING SYSTEMS, 2001, 20 (03) : 188 - 197

← 1 2 3 4 5 →