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IC Engine Dynamic oil Life Prediction Using Machine Learning Approach
Technical Paper
2022-28-0025
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Conventional resource conservation for automotive traction and its utilization to maximize efficiency is not only important but also essential for climate change. Diesel engine lubricating oil is one such component, which has enough potential considering the current methods of servicing. Present work is focused on developing/validating a process, which can predict critical properties of oil at any given instance. This prediction is independent of vehicle operating conditions and vehicle mileage. As the process is independent of above two parameters, even the oil quality after a top up can be identified. Experimental setup consisted of single cylinder stationary engine (Kirloskar - TV1) delivering 5.2kW power @1500 RPM is tested for detailed analysis of the oil characteristics such as Kinematic viscosity, Acid number, Base number and Carbon residue with respect to different engine parameters.Test run was conducted for 150 hrs and the said characteristics for samples were critically analysed at fixed intervals. A ML model was built using data obtained from the test to classify oil quality and predict the remaining mileage. This model was validated with different oil samples (fresh, intermediate and deteriorated). While this model was deployed over raspberry pi for live computations, the results (classification - ok/not ok and mileage) were relayed to a mobile application to simulate end user real time monitoring. The future work will focus on real time testing of a heavy diesel engine integrating both experimental and ML approaches to predict the life of lubricating oil coupled with dynamic scenarios and potential oil failure modes.
Authors
- Santosh Jangamwadimath - Daimler Trucks Innovation Center India
- Chirantan Gayakwad - KLE Technological University
- Nagaraj Banapurmath - KLE Technological University
- Ashwin Kubasadgoudar - KLE Technological University
- Vishal Pattanashetty - KLE Technological University
- Shashwat Suyash - KLE Technological University
- Nalini Iyer - KLE Technological University
- Shashidhar Shiva - Daimler Trucks Innovation Center India
- Priyamvad Priyadarshi - Daimler Trucks Innovation Center India
Topic
Citation
Jangamwadimath, S., Gayakwad, C., Banapurmath, N., Kubasadgoudar, A. et al., "IC Engine Dynamic oil Life Prediction Using Machine Learning Approach," SAE Technical Paper 2022-28-0025, 2022, https://doi.org/10.4271/2022-28-0025.Also In
References
- Zhang , D. and Lamon , D. External Variables that Alter Engine Oil Life Monitoring Systems in On-Road Fleets SAE/KSAE 2013 International Powertrains, Fuels and Lubricants Meeting 2013 https://doi.org/10.4271/2013-01-2607
- Ibrahim , D. , Stapah , M. , Ruslan , M.A.A. , Yaakob , Y. et al. Predicting the Next Oil Change for Automotive Engine Oil 2018 10.1088/1742-6596/1349/1/012018
- Keartland , S. and van Zyl , T. Automating Predictive Maintenance Using Oil Analysis and Machine Learning 2020 10.1109/SAUPEC/RobMech/PRASA48453.2020.9041003
- Raposo , H. , Farinha , J. , Fonseca , I. , and Ferreira , L. Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses 10.3390/act8010014
- Tanwar , M. and Raghavan , N. Lubricating Oil Remaining Useful Life Prediction Using Multi-Output Gaussian Process Regression IEEE Access 2020 10.1109/ACCESS.2020.3008328
- Rodrigues , J. , Cost , I. , Farinha , J. , Mendes , M. et al. Predicting Motor Oil Condition Using Artificial Neural Networks and Principal Component Analysis Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020 10.17531/ein.2020.3.6
- Jun , H.-B. , Kiritsis , D. , Gambera , M. , and Xirouchakis , P. Predictive Algorithm to Determine the Suitable Time to Change Automotive Engine Oil Computers & Industrial Engineering 51 4 2006 671 683 10.1016/j.cie.2006.06.017
- Theissler , A. , Pérez-Velázquez , J. , Kettelgerdes , M. , and Elger , G. Predictive Maintenance Enabled by Machine Learning: Use Cases and Challenges in the Automotive Industry Reliability Engineering & System Safety 215 2021 107864 10.1016/j.ress.2021.107864
- Jagannathan , S. and Raju , G.V.S. Remaining Useful Life Prediction of Automotive Engine Oils Using MEMS Technologies Proceedings of the 2000 American Control Conference ACC (IEEE Cat. No. 00CH36334) 3511 3512 5 2000 10.1109/ACC.2000.879222
- Jardine , A.K. , Lin , D. , and Banjevic , D. A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance Mechanical Systems and Signal Processing 20 7 2006 1483 1510 10.1016/j.ymssp.2005.09.012
- Zhu , X. , Zhong , C. , and Zhe , J. Lubricating Oil Conditioning Sensors for Online Machine Health Monitoring a Review Tribol. Int. 109 2017 473 484
- Ebersbach , S. , Peng , Z. , and Kessissoglou , N. Smart Condition Monitoring by Integration of Vibration Oil and Wear Particle Analysis Proceeding 14th International Congress on Sound and Vibration Cairns, Australia 2007 1 9
- Wakiru , J. , Pintelon , L. , Muchiri , P.N. , and Chemweno , P. A Statistical Approach for Analyzing Used Oil Data and Enhancing Maintenance Decision Making: Case Study of a Thermal Power Proceedings of 2nd International Conference on Maintenance Engineering (Income-II) 2017 117 128
- Phillips , J. , Cripps , E. , Lau , J.W. , and Hodkiewicz , M.R. Classifying Machinery Condition Using Oil Samples and Binary Logistic Regression Mech. Syst. Signal Process. 60-61 2015 316 325
- Caesarendra , W. , Widodo , A. , and Yang , B.-S. Application of Relevance Vector Machine and Logistic Regression for Machine Degradation Assessment Mech. Syst. Signal Process. 24 4 2010 1161 1171
- Wakiru , J. , Pintelon , L. , Chemweno , P. , and Munchiri , P.N. A Decision Tree-Based Classifcation Framework for Used Oil Analysis Applying Random Forest Feature Selection J. Appl. Sci. Eng. Technol. Develop. 3 2018 90 100
- Du , Y. , Wu , T. , and Makis , V. Parameter Estimation and Remaining Useful Life Prediction of Lubricating Oil with HMM Wear 376-377 2017 1227 1233
- Kumar , S. , Mukherjee , P. , and Mishra , N. Online Condition Monitoring of Engine Oil Industrial Lubrication and Tribology 57 6 2005 260 267 10.1108/00368790510622362
- Bansal , D. , Evans , D. , and Jones , J. A Real-Time Predictive Maintenance System for Machine Systems International Journal of Machine Tools and Manufacture 44 2004 759 766
- Bommareddi , A. 2009