FAULT PREDICTION BY USING DATA MINING APPROACH

Authors

  • Chanchal Chauhan M.E. Student, Production & industrial engineering Department PEC University of technology
  • Dr. Parveen Kalra Professor, Production & industrial engineering Department PEC University of technology
  • Dr. C.S. Jawalkar Associate Professor, Production & industrial engineering Department PEC University of technology

Keywords:

Data mining, Fault prediction, Artificial Neural Network, Decision tree, Quality control

Abstract

Data mining is an effective tool which can be used in decision making in the organization. In this research, data mining
is applied in the JCB Ballabgarh plant for the prediction of faults in the parts of machinery manufactured at JCB. Data is
collected and retrieved from the quality department of the JCB and then it is transformed into the format which can be used in
data mining tool i.e. rattle. Many variables were added to the dataset and many steps were done to make data useful for data
mining. Data mining algorithms were applied on the data which are decision tree, support vector machine (SVM), Artificial
Neural Network (ANN). From the results it has been found that ANN showed very high accuracy in the fault prediction while
decision tree was good at predicting but SVM showed poor performance in the fault prediction. It have been showed that data
mining is an excellent tool for making prediction than the traditional statistical methods.

Downloads

Published

2015-07-25

How to Cite

Chanchal Chauhan, Dr. Parveen Kalra, & Dr. C.S. Jawalkar. (2015). FAULT PREDICTION BY USING DATA MINING APPROACH. International Journal of Advance Research in Engineering, Science & Technology, 2(7), 106–111. Retrieved from https://ijarest.org/index.php/ijarest/article/view/261

Issue

Vol. 2 No. 7 (2015): Volume: 02, Issue: 07 - July, 2015.

Section

Articles