Predictive Maintenance Approach on Automobiles

Authors

  • Patel p Computer Engineering, Modern Education Society’s College of Engineering, Savitribai Phule Pune University, Pune, India
  • Jain P Computer Engineering, Modern Education Society’s College of Engineering, Savitribai Phule Pune University, Pune, India
  • Bhambure S Computer Engineering, Modern Education Society’s College of Engineering, Savitribai Phule Pune University, Pune, India
  • Sen Y Computer Engineering, Modern Education Society’s College of Engineering, Savitribai Phule Pune University, Pune, India
  • Shaikh NF Computer Engineering, Modern Education Society’s College of Engineering, Savitribai Phule Pune University, Pune, India

DOI:

https://doi.org/10.26438/ijcse/v6i12.763767

Keywords:

Predictive maintenance, machine learning automobiles, AZURE

Abstract

The main purpose of this paper is exploring the fact that how to use a machine learning model in order to perform predictive maintenance on Automobile. Maintenance and Care play a key role in the smooth and safe running of your motorcycle. The goal is to predict when the automobile require service or maintenance. If the model runs successfully, it gives us enough data about determining what the problem is and not only providing the necessary solutions but also ordering the parts and scheduling the people necessary to repair it. The innovative solutions of Predictive Maintenance recursively monitor, evaluates and report the component and system conditions in the vehicle. Various techniques are discussed and tested, such as linear and quantile regression. The primary aim of the system is to increase the vehicle’s efficiency due to the observed and supervised driving behavior which is able to minimize the fuel consumptions and exhaust. Based on received data from the various connected vehicle and transmitting it to the cloud i.e. Azure where the processing of the data takes place, errors are predicted and fixed before time and with less damage of vehicle whereby reducing the overall cost of maintenance.

References

[1] Jong-Ho Shin and Hong-Bae Jun. “On condition based maintenance policy”. Journal of Computational Design and Engineering, 2(2): pp.119–127, 2015.

[2] R. Kothamasu, S. H. Huang, and W. H. VerDuin.” System health monitoring and prognostics — a review of current paradigms and practices”. The International Journal of Advanced Manufacturing Technology, 28(9), pp. 1012–1024, 2006.

[3] Ying Peng, Ming Dong, and Ming Jian Zuo. “Current status of machine prognostics in condition-based maintenance”: a review. The International Journal of Advanced Manufacturing Technology, pp. 297–313, 2010.

[4] Xunyuan Yin, Zhaojian Li, Sirish L. Shah, Lisong Zhang, Changhong Wang , “Fuel Efficiency Modelling and Prediction for Automotive Vehicles: A Data-Driven Approach”, 2015 IEEE International Conference on Systems, Man, and Cybernetics, Chain ,pp 2527-2532, 2015.

[5] Rohit Dhall, Vijender Solanki, “An IoT Based Predictive

Connected Car Maintenance Approach”, International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, Nº3,pp. 1622, 2017.

[6] Gian Antonio Susto, Andrea Schirru, Simone Pampuri, Se´an McLoone Senior Member, IEEE, Alessandro Beghi Member, IEEE, “Machine Learning for Predictive Maintenance: a Multiple Classifier Approach”, IEEE Transactions on Industrial Informatics, 11(3), 812-820, pp.1-8 , 2014.

[7] Emir Husni , Galuh Boy Hertantyo , Daniel Wahyu , Muhamad

Agus Triawan , “Applied Internet Of Things (IoT):Car monitoring system using IBM BlueMix”, 2016 International Seminar on Intelligent Technology and Its Application©2016 IEEE, Indonesia, pp. 417-422, 2016.

[8] Robert H. Shumway and David S. Stoffer. “Time series analysis and its applications: with R examples. Springer”, New York,USA, 2nd [update];second; edition, 2006

Downloads

Published

2018-12-31
CITATION
DOI: 10.26438/ijcse/v6i12.763767
Published: 2018-12-31

How to Cite

[1]
P. Patel, P. Jain, S. Bhambure, Y. Sen, and N. Shaikh, “Predictive Maintenance Approach on Automobiles”, Int. J. Comp. Sci. Eng., vol. 6, no. 12, pp. 763–767, Dec. 2018.

Issue

Vol. 6 No. 12 (2018): IJCSE December Editor

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.