Revisión Del Proceso De La Degradación en Los Aceites Lubricantes en Motores De Gas Natural Comprimido Y Diesel

Martínez, Vicente Macián; Tormos, Bernardo; Gómez, Y. A.; Tamarit, Vicente Bermudez

doi:10.6036/5077

Cited by 8 publications

(1 citation statement)

References 9 publications

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“…According to (Macián‐Martínez et al, 2013) the main parameters related to oil degradation are: Oxidation —undesirable series of chemical reactions involving oxygen that degrades the quality of an oil; Nitration —undesirable degradation of engine oil that occurs in a reduced oxygen environment. It causes deposit forming and can eventually reduce engine performance and damage engine parts; Viscosity —is the term used for the fluidity of the lubricant as a function of a given temperature.…”

Section: Related Workmentioning

confidence: 99%

A supervised machine learning model for determining lubricant oil operating conditions

2022

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Machine learning tools for analysing lubricating oil data have enabled better information in the condition based maintenance (CBM) approach to the process of diagnosing and predicting failures in diesel‐powered vehicle fleets. With the increase in the number of sensors inserted in vehicles, it is possible for companies to stockpile large quantities of information in real time. As such, the development of data‐driven tools will enable accurate identification of the level of wear of a system, evolving CBM into a more reliable and dynamic approach. Following this type of data‐based analysis focusing on determining the wear of systems and equipment, this paper presents an intelligent system for assessing the condition of lubricating oil in automotive diesel engines. To this end, we analyse the use of raw data obtained from the sensors installed in the car and evaluate in conjunction with the insertion of engineered features designed the best way to determine the operating state of the oils. The results presented in this analysis show that to explain 90% of the variation in the original data only the variables kinematic viscosity, dynamic viscosity, engine oil temperature and OSF_v3 are needed. After evaluating the quality of the variables, we conducted an experimental study to analyse the performance of various machine learning algorithms, taking into account the number of features as input data. The results show that the proposed system has the ability to identify the operating conditions of lubricating oil using seven variables as input to a model based on gradient boosting, obtaining a recall result of 93%, precision of 96% and F1‐score of 94%. We conducted a set of additional studies to understand how different subsets of variables affected the performance of the models, and the results show that the best combination includes information regarding the engine speed, coolant and oil temperature, oil pressure, the oil stress factor (OSF_v3), kinematic viscosity, and the dynamic viscosity.

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Section: Related Workmentioning

confidence: 99%

A supervised machine learning model for determining lubricant oil operating conditions

2022

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Findings from a fleet test on the performance of two engine oil formulations in automotive CNG engines

Macián

Tormos

Olmeda

et al. 2013

Lubrication Science

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This work presents a comparative assessment of engine oil performance on field test using urban transport vehicles powered by CNG engines using two different mineral oil formulations approved by engine manufacturer. The first one is considered as a baseline reference, and the second one is a higher quality formulation in terms of base-stock refining and additive content. Higher quality oil has shown a significant enhanced lubricant performance, leading to reach the oil drain interval defined by engine manufacturer on these engines without penalties in maintenance costs.In order to assess oil performance, an oil analysis program has been established for oil samples collected from vehicles operated under real service conditions in an urban transport fleet. Monitored parameters include: oxidation, nitration, aminic antioxidant additives depletion, anti-wear additives depletion, TAN, TBN and RUL number (as an estimation of antioxidant additive depletion including aminic and ZDDP).Results obtained in more than 90 samples from 15 different vehicles have shown higher degradation rates for low quality lubricant oil formulation. This deviation can be explained taking into account factors related with lower antioxidant additives content and lower thermal stability that can be mainly related with the base stock quality. This lower oil performance can be finally converted into higher vehicle maintenance cost and lower engine reliability

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Wear and Tear: A Data Driven Analysis of the Operating Condition of Lubricant Oils

Malaguti¹,

Lourenço²,

Silva³

2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Revisión Del Proceso De La Degradación en Los Aceites Lubricantes en Motores De Gas Natural Comprimido Y Diesel

Abstract: Revisión del proceso de la degradación en los aceites lubricantes en motores de gas natural comprimido y diesel

Cited by 8 publications

References 9 publications

A supervised machine learning model for determining lubricant oil operating conditions

A supervised machine learning model for determining lubricant oil operating conditions

Findings from a fleet test on the performance of two engine oil formulations in automotive CNG engines

Wear and Tear: A Data Driven Analysis of the Operating Condition of Lubricant Oils

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