Monitoring internal combustion engine wear from engine oil using linear regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00564782" target="_blank" >RIV/60162694:G43__/26:00564782 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie" target="_blank" >https://www.spiedigitallibrary.org/conference-proceedings-of-spie</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.3083483" target="_blank" >10.1117/12.3083483</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Monitoring internal combustion engine wear from engine oil using linear regression

Popis výsledku v původním jazyce

This paper deals with the wear monitoring of an internal combustion engine in a heavy combat vehicle. For the purpose of the study, engine oil samples were collected and then checked using a technique called atomic emission spectroscopy (AES). This is one of the modern ways of measuring metallic elements in oil, including iron. The amount of iron in engine oil is expressed in parts per million (ppm). This indicates how much the metal parts of an internal combustion engine are worn. Engine oil samples from 15 combat vehicles over several years were used for the experiment. The aim of this work was to predict the iron concentration in engine oil using a suitable mathematical method. Robust linear regression with MM estimator is used in this paper. It is a method that combines high robustness to outliers and minimizes a weighted loss function using an iterative optimization approach. The second method used is the least squares method, which is a mathematical technique that works by minimizing the sum of the squares of the differences between the actual values and the values predicted by the model.

Název v anglickém jazyce

Monitoring internal combustion engine wear from engine oil using linear regression

Popis výsledku anglicky

This paper deals with the wear monitoring of an internal combustion engine in a heavy combat vehicle. For the purpose of the study, engine oil samples were collected and then checked using a technique called atomic emission spectroscopy (AES). This is one of the modern ways of measuring metallic elements in oil, including iron. The amount of iron in engine oil is expressed in parts per million (ppm). This indicates how much the metal parts of an internal combustion engine are worn. Engine oil samples from 15 combat vehicles over several years were used for the experiment. The aim of this work was to predict the iron concentration in engine oil using a suitable mathematical method. Robust linear regression with MM estimator is used in this paper. It is a method that combines high robustness to outliers and minimizes a weighted loss function using an iterative optimization approach. The second method used is the least squares method, which is a mathematical technique that works by minimizing the sum of the squares of the differences between the actual values and the values predicted by the model.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of SPIE - The International Society for Optical Engineering

ISBN

978-1-5106-9521-4

ISSN

0277-786X

e-ISSN

1996-756X

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

—

Místo konání akce

Singapore, Republic of Singapore

Datum konání akce

30. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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