Analysis of contamination in a failing diesel engine using thermography

Main Article Content

Cristian García
José Molina
José Segnini
Mary Vergara
Néstor Rivera

Abstract

Vehicle emissions legislation is becoming stricter as it aims to minimize the impact of internal combustion engines on the environment. These emissions change drastically when there are faults. This research focuses on defining the relationships between data that represent the failure conditions in a turbocharged diesel engine through thermographic analysis, considering the quantity of particles and opacity. There have been 45 types of failures associated with the opening of the exhaust gas recirculation valve (EGR) and restriction in the exhaust with different engine speeds. To these data, we have analyzed the mean with its standard deviation, the root mean square (RMS), statistical significance and correlation to determine which variables are strongly correlated. The results obtained show that the most relevant statistical parameters that characterize the induced faults are: the maximum and minimum values of temperature, the mean and the RMS. It is also observed that, if the opening of the EGR and increased the revolutions per minute or the restriction area in the exhaust decreases, the pollution increases.

Downloads

Download data is not yet available.

Article Details

How to Cite
GarcíaC., MolinaJ., SegniniJ., VergaraM., & RiveraN. (2019). Analysis of contamination in a failing diesel engine using thermography. AXIOMA, (19), 48-57. Retrieved from https://axioma.pucesi.edu.ec/index.php/axioma/article/view/541
Section
INVESTIGACIÓN
Author Biographies

Cristian García, Universidad Politécnica Salesiana

Universidad Politécnica Salesiana, Ingeniería Automotriz, Grupo en Ingeniería de Transporte

José Molina, Universidad de Los Andes

Universidad de Los Andes, Facultad de Ingeniería, Grupo de Diseño y Modelado de Máquinas. DIMMA

José Segnini, Pontificia Universidad Católica de Ecuador.

Pontificia Universidad Católica de Ecuador. Sede Ibarra. Escuela de Diseño. Grupo de Investigación en Diseño Sustentable.
GIDISUS

Mary Vergara, Universidad de Los Andes y Universidad de Nacional de Loja.

Universidad de Los Andes, Facultad de Ingeniería, Grupo de Diseño y Modelado de Máquinas. DIMMA
Universidad de Nacional de Loja, Facultad De La Energía, las Industrias y los Recursos Naturales No Renovables. Carrera
de Ingeniería en Mecánica Automotriz.

Néstor Rivera, Universidad Politécnica Salesiana

Universidad Politécnica Salesiana, Ingeniería Automotriz, Grupo en Ingeniería de Transporte

References

Arkadiusz R., y Malgorzata J. (2014). Diagnostics Systems as a Tool to Reduce and Monitor Gas Emissions from Combustion Engines, In: Golinska, P. (ed.) Environmental Issues in Automotive Industry, 95–128.
Liu, X., Feng, F. y Si, A. (2012). Condition Based Monitoring, Diagnosis and Maintenance On Operating Equipments of a Hydraulic Generator Unit. IOP Publishing, 24th IAHR Symposium on Hydraulic Machinery and Systems. 15(4), 1755-1315.
Mantilla L., Christian A., Tapia J., Carlos R. (2015). Estudio de los efectos de la apertura de la válvula EGR en la combustión de un motor de encendido por comprensión CRDI, mediante el uso de termografía infrarroja. (Tesis de Grado). Universidad Politecnica Salesiana, Quito, Ecuador.
Monieta, J. (2018). The use of thermography in the diagnosis of ship piston internal combustion engines. In MATEC Web of Conferences, 182, p. 01027. EDP Sciences.
Rodriguez, B. (2014). Modelling and Observation of Exhaust Gas Concentrations for Diesel Engine Control. (Springer Theses). Universitat Politècnica de València, Spain. Switzerland
The IMS Center University of Cincinnati. (2014). Development of Smart Prognostics Agents (WATCHDOG AGENT®). National Science Foundation (NSF) Industry y University Cooperative Research Center for Intelligent Maintenance Systems. Recuperado de http://www.imscenter.net/frontpage/Resources/WD.pdf National Instruments (2016).
Vibration Analysis and Signal Processing in LabVIEW. National Instruments. Recuperado de http://www.ni.com/white-paper/9230/en/
Van Tran, T. y Bo-Suk, Y. (2012). An Intelligent Condition-Based Maintenance Platform for Rotating Machinery. Expert Systems with Applications. Elsevier, 39(3). 2977-2988.