TY  - JOUR
N1  - cited By 0; Conference of 15th ISEAM flagship World Congress on Engineering Asset Management, WCEAM 2021 ; Conference Date: 15 August 2021 Through 18 August 2021; Conference Code:276019
N2  - 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 â??), 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.
KW  - Forecasting; Lubricating oils; Lubrication; Mean square error; Neural networks
KW  -  Age predictions; Correlation analysis; Gram-schmidt; Kinematics viscosity; Lubrication /; Lubrication oil; Mahalanobis; Monitoring indicators; Oil analysis; Prediction modelling
KW  -  Oxidation
SP  - 411
ID  - scholars17699
TI  - Developing a Lubrication Oil Age Prediction Model
AV  - none
UR  - 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
A1  - Mohammad Nazari, N.
A1  - Muhammad, M.
JF  - Lecture Notes in Mechanical Engineering
EP  - 421
Y1  - 2022///
SN  - 21954356
PB  - Springer Science and Business Media Deutschland GmbH
ER  -