A Predictive Algorithm for Estimating the Quality of Vehicle Engine Oil

Authors

  • Hong-Bae Jun Hongik University
  • Fabrizio Lo Conte Ecole Polytechnique Fédérale de Lausanne
  • Dimitris Kiritsis Ecole Polytechnique Fédérale de Lausanne
  • Paul Xirouchakis Ecole Polytechnique Fédérale de Lausanne

DOI:

https://doi.org/10.23055/ijietap.2008.15.4.186

Keywords:

Predictive maintenance, Degradation, Engine oil, Statistical methods.

Abstract

Recently, with emerging technologies, visibility of vehicle information over the whole lifecycle becomes possible. The visibility opens up new challenging issues for improving the efficiency of vehicle operations. One of the most challenging problems arising during the middle of life (MOL) of vehicles is the predictive maintenance on engine oil. For this, in this study, we focus on developing a predictive algorithm to estimate the quality of the engine oil of a vehicle by analyzing its degradation status with mission profile data. For this purpose, we specify the relations between indicators of engine mission profiles and oil quality indicators using principal component analysis and regression method. Then, we develop a heuristic algorithm for estimating the value of a quality indicator of engine oil based on them. To evaluate the proposed approach, we carry out a case study and computational experiments.

Author Biographies

Hong-Bae Jun, Hongik University

H

Fabrizio Lo Conte, Ecole Polytechnique Fédérale de Lausanne

Fabrizio Lo Conte received is Master of Science degree in Micro engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, in 2006. He is a doctoral student of EPFL. He is now working with the Electronics Laboratory of EPFL, supervised by Professor Maher Kayal. His research topic is in high-voltage and high temperature micro electronics modeling, parastics current estimation, and isolation structure topology analysis.

Dimitris Kiritsis, Ecole Polytechnique Fédérale de Lausanne

Dr. Dimitris Kiritsis has got his Diploma (1980) and Ph.D. (1987) in Mechanical Engineering from the University of Patras, Greece. Since 1989 he is with the Computer-Aided Design and Production Laboratory (LICP) of the Swiss Federal Institute of Technology in Lausanne (EPFL). His principal investigations include: (i) an original method for integrated and dynamic manufacture /assembly/ disassembly process planning modeling and simulation using Petri nets and (ii) product life cycle information modeling and management.

Paul Xirouchakis, Ecole Polytechnique Fédérale de Lausanne

Professor Paul Xirouchakis is directing the computer-aided design and computer-aided manufacturing (CAD/CAM) laboratory, institute of production and robotics at the Swiss Federal Institute of Technology in Lausanne, Switzerland. He obtained his Ph.D. in Structural Mechanics in 1978 from Massachusetts Institute of Technology. His research interests are in the areas of (i) product modeling and reasoning for manufacture/assembly/remanufacture (ii) manufacturing information systems and (iii) informatics for planning and scheduling for manufacture/assembly/remanufacture.

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Published

2022-02-26

How to Cite

Jun, H.-B., Conte, F. L., Kiritsis, D., & Xirouchakis, P. (2022). A Predictive Algorithm for Estimating the Quality of Vehicle Engine Oil. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(4), 386–396. https://doi.org/10.23055/ijietap.2008.15.4.186

Issue

Vol. 15 No. 4 (2008)

Section

Modelling and Simulation

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