Ajay-IJPR-1597.R1.pdf (379.93 kB)

A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

Download (379.93 kB)
journal contribution
posted on 07.11.2016, 15:55 by Ajay Kumar, Ravi Shankar, Alok Choudhary, Lakshman S. Thakur
This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in cloud-based manufacturing is handling of datasets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such datasets. The framework proposed in this research uses a hybrid approach to deal with big dataset for smarter decisions. Furthermore, we compare the performance of radial basis function based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in cloud-based manufacturing, is to predict the effect of data errors on quality due to highly imbalance unstructured dataset. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones.

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Production Research

Volume

54

Issue

23

Pages

7060 - 7073

Citation

KUMAR, A. ...et al., 2016. A big data MapReduce framework for fault diagnosis in cloud-based manufacturing. International Journal of Production Research, 54(23), pp. 7060-7073.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016-03-04

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 4th March 2016, available online: http://www.tandfonline.com/10.1080/00207543.2016.1153166.

ISSN

0020-7543

eISSN

1366-588X

Language

en

Exports