Reducing the Costs of Using Construction Machinery by Using the Oil Condition Monitoring Technique

Document Type : Original Article

Author

Department of Civil Engineering, Payame Noor University, Tehran, Iran.

Abstract

In this research, with the aim of reducing the costs of maintenance and use of construction machinery in the road construction sector, the methods of increasing the time intervals for changing the engine oil of these machines have been discussed. Accordingly, as a case study, this goal was achieved in an excavator of the Komatsu PC220-7 model using SAA6D102E-2-C engine oil by means of an engine oil condition monitoring system that was used at intervals of 55, 110 and 165 hours of operation. Machines have been investigated in construction and road construction projects. The main parameters of this research include viscosity at 40 degrees Celsius, alkaline number, erosive elements (iron, chromium, aluminum, copper, lead), oil additives (zinc, phosphorus, calcium), oil contaminants (calcium, sodium and Boron) and Particle Quantifier for each of which a standard index has been considered and the change of these parameters in three periods of 55, 110 and 165 hours of vehicle engine operation has been investigated. Also, as an economic discussion, the cost parameters in Iran for these conditions have been investigated. The results of this research showed the good performance of the oil during 165 hours of engine operation.

Keywords

Main Subjects

Copyright © 2022 Tavakol Rajabi. This is an open access paper distributed under the Creative Commons Attribution License. Journal of Civil Engineering and Materials Application is published by Pendar Pub; Journal p-ISSN 2676-332X; Journal e-ISSN 2588-2880.

 

References

[1] Zhu X, Zhong C, Zhe J. Lubricating oil conditioning sensors for online machine health monitoring–A review. Tribology International. 2017 May 1;109:473-84. [View at Google Scholar]; [View at Publisher].
[2] Agoston A, Ötsch C, Jakoby B. Viscosity sensors for engine oil condition monitoring—Application and interpretation of results. Sensors and Actuators A: Physical. 2005 Jun 30;121(2):327-32. [View at Google Scholar]; [View at Publisher].
[3] Duan C, Deng C, Gharaei A, Wu J, Wang B. Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions. International Journal of Production Research. 2018 Dec 2;56(23):7160-78. [View at Google Scholar]; [View at Publisher].
[4] Schexnayder Jr CJ. Heavy construction equipment replacement economics (Doctoral dissertation, Purdue University). [View at Google Scholar]; [View at Publisher].
[5] Rao X, Sheng C, Guo Z, Yuan C. A review of online condition monitoring and maintenance strategy for cylinder liner-piston rings of diesel engines. Mechanical Systems and Signal Processing. 2022 Feb 15;165:108385. [View at Google Scholar]; [View at Publisher].
[6] Han Y, Song YH. Condition monitoring techniques for electrical equipment-a literature survey. IEEE Transactions on Power delivery. 2003 Jan 14;18(1):4-13. [View at Google Scholar]; [View at Publisher].
[7] Zhou W, Habetler TG, Harley RG. Bearing condition monitoring methods for electric machines: A general review. In2007 IEEE international symposium on diagnostics for electric machines, power electronics and drives 2007 Sep 6 (pp. 3-6). IEEE. [View at Google Scholar]; [View at Publisher].
[8] Tavner PJ. Review of condition monitoring of rotating electrical machines. IET electric power applications. 2008 Jul 1;2(4):215-47. [View at Google Scholar]; [View at Publisher].
[9] Karballaeezadeh N, Mohammadzadeh S D, Shamshirband S, Hajikhodaverdikhan P, Mosavi A, Chau KW. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Engineering Applications of Computational Fluid Mechanics. 2019 Jan 1;13(1):188-98. [View at Google Scholar]; [View at Publisher].
[10] Ji B, Zhang X, Mumtaz S, Han C, Li C, Wen H, Wang D. Survey on the internet of vehicles: Network architectures and applications. IEEE Communications Standards Magazine. 2020 Mar;4(1):34-41. [View at Google Scholar]; [View at Publisher].
[11] Cheng JC, Chen W, Chen K, Wang Q. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction. 2020 Apr 1;112:103087. [View at Google Scholar]; [View at Publisher].
[12] Yang W, Tavner PJ, Crabtree CJ, Feng Y, Qiu Y. Wind turbine condition monitoring: technical and commercial challenges. Wind Energy. 2014 May;17(5):673-93. [View at Google Scholar]; [View at Publisher].
[13] Prajapati A, Bechtel J, Ganesan S. Condition based maintenance: a survey. Journal of Quality in Maintenance Engineering. 2012 Oct 19;18(4):384-400. [View at Google Scholar]; [View at Publisher].
[14] Newell GE. Oil analysis cost‐effective machine condition monitoring technique. Industrial Lubrication and tribology. 1999 Jun 1;51(3):119-24. [View at Google Scholar]; [View at Publisher].
[15] Poley J. Reciprocating Engine Oil Analysis (EOA)‖. InCTC Analytical Services, Papers From Practicing Oil Analysis 2000 Conference 2000 (pp. 93-100). [View at Google Scholar]; [View at Publisher].
[16] Raposo H, Farinha JT, Fonseca I, Ferreira LA. Condition monitoring with prediction based on diesel engine oil analysis: A case study for urban buses. InActuators 2019 Feb 16 (Vol. 8, No. 1, p. 14). MDPI. [View at Google Scholar]; [View at Publisher].
[17] Zhao J, Wang D, Zhang F, Liu Y, Chen B, Wang ZL, Pan J, Larsson R, Shi Y. Real-time and online lubricating oil condition monitoring enabled by triboelectric nanogenerator. ACS nano. 2021 Jun 25;15(7):11869-79. [View at Google Scholar]; [View at Publisher].
[18] AlShorman O, Irfan M, Saad N, Zhen D, Haider N, Glowacz A, AlShorman A. A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock and vibration. 2020 Nov 4;2020:1-20. [View at Google Scholar]; [View at Publisher].
[19] Wakiru JM, Pintelon L, Muchiri PN, Chemweno PK. A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical systems and signal processing. 2019 Mar 1;118:108-32. [View at Google Scholar]; [View at Publisher].
[20] Ahmadi, H., & Salami, P. Determination of oil life for crane Liebherr Model D9408 diesel engine by Oil Condition Monitoring. [View at Google Scholar]; [View at Publisher].
 
Journal of Civil Engineering and Materials Application
Volume 6, Issue 4
December 2022
Pages 221-228

Files

History

  • Receive Date: 06 September 2022
  • Revise Date: 14 November 2022
  • Accept Date: 29 November 2022

Share

How to cite

Statistics