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Developing a Lubrication Oil Age Prediction Model

In this study, lubrication oil age is predicted based on selected monitoring indicators. The information that was extracted from the oil analysis report are the TBN, oxidation, kinematic viscosity (100 �), contaminants and elemental analysis. Correlation analysis was applied to the data to assess...

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Main Authors: Mohammad Nazari, N., Muhammad, M.
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127699572&doi=10.1007%2f978-3-030-96794-9_38&partnerID=40&md5=7e87a9e5de923f07f2283c3578cc857e
http://eprints.utp.edu.my/33270/
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spelling my.utp.eprints.332702022-07-26T06:31:59Z Developing a Lubrication Oil Age Prediction Model Mohammad Nazari, N. Muhammad, M. In this study, lubrication oil age is predicted based on selected monitoring indicators. The information that was extracted from the oil analysis report are the TBN, oxidation, kinematic viscosity (100 �), contaminants and elemental analysis. Correlation analysis was applied to the data to assess the relationship between the lubrication parameters and oil age. Based on the analysis, oxidation was identified to have high correlation with oil age. Mahalanobis-Taguchi Gram Schmidt (MTGS) method was applied to identify the critical variable to predict oil age. Based on the MTGS analysis, TBN, oxidation, Pb and Mo have a positive SN ratio gain and were selected to be included in the lubrication oil age prediction model. The study demonstrates the lubrication oil age prediction model based on Artificial neural network (ANN) with TBN, oxidation, Pb and Mo as predictor variables with an R squared of 0.8176, mean square error (MSE) and mean absolute deviation (MAD) of 1191 and 26 respectively. Based on the available sample data and threshold value, it can also be observed that readings of the lubrication oil parameters are still within limits after the recommended duration for lubrication oil to be in service. These findings are beneficial for future works to predict the remaining useful life of lubrication oil. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Springer Science and Business Media Deutschland GmbH 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127699572&doi=10.1007%2f978-3-030-96794-9_38&partnerID=40&md5=7e87a9e5de923f07f2283c3578cc857e Mohammad Nazari, N. and Muhammad, M. (2022) Developing a Lubrication Oil Age Prediction Model. Lecture Notes in Mechanical Engineering . pp. 411-421. http://eprints.utp.edu.my/33270/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description In this study, lubrication oil age is predicted based on selected monitoring indicators. The information that was extracted from the oil analysis report are the TBN, oxidation, kinematic viscosity (100 �), contaminants and elemental analysis. Correlation analysis was applied to the data to assess the relationship between the lubrication parameters and oil age. Based on the analysis, oxidation was identified to have high correlation with oil age. Mahalanobis-Taguchi Gram Schmidt (MTGS) method was applied to identify the critical variable to predict oil age. Based on the MTGS analysis, TBN, oxidation, Pb and Mo have a positive SN ratio gain and were selected to be included in the lubrication oil age prediction model. The study demonstrates the lubrication oil age prediction model based on Artificial neural network (ANN) with TBN, oxidation, Pb and Mo as predictor variables with an R squared of 0.8176, mean square error (MSE) and mean absolute deviation (MAD) of 1191 and 26 respectively. Based on the available sample data and threshold value, it can also be observed that readings of the lubrication oil parameters are still within limits after the recommended duration for lubrication oil to be in service. These findings are beneficial for future works to predict the remaining useful life of lubrication oil. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
format Article
author Mohammad Nazari, N.
Muhammad, M.
spellingShingle Mohammad Nazari, N.
Muhammad, M.
Developing a Lubrication Oil Age Prediction Model
author_facet Mohammad Nazari, N.
Muhammad, M.
author_sort Mohammad Nazari, N.
title Developing a Lubrication Oil Age Prediction Model
title_short Developing a Lubrication Oil Age Prediction Model
title_full Developing a Lubrication Oil Age Prediction Model
title_fullStr Developing a Lubrication Oil Age Prediction Model
title_full_unstemmed Developing a Lubrication Oil Age Prediction Model
title_sort developing a lubrication oil age prediction model
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127699572&doi=10.1007%2f978-3-030-96794-9_38&partnerID=40&md5=7e87a9e5de923f07f2283c3578cc857e
http://eprints.utp.edu.my/33270/
_version_ 1739833199005007872
score 13.211869

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