Study and Analysis of Different Data Mining Algorithms Classification for Fault Detection

  • P. Rama Devi, D. Srinivasa Rao, J. Malathi, Dr. K. Parish Venkata Kumar

Abstract

This paper contrasts three classifiers (SVM, WKNN) with the performance of the decision treaties using data from optimized and not optimized sensor solutions. This article compares the performance of three classifiers. The algorithms are equipped with known data and then evaluated for various scenarios with varying degrees of severity. In order to achieve a high degree of reliability, improvement of the product life cycle is necessary. In general, repair operations have as their purpose the elimination and minimization of failures of industrial machinery. By different fault detection techniques the industrial companies are trying to improve their efficiency. One solution is to process and evaluate data generated beforehand in order to avoid future failures. The purpose of this paper is to detect waste parts using various data mining algorithms and to compare their accuracy.

Published
2020-05-07
How to Cite
P. Rama Devi, D. Srinivasa Rao, J. Malathi, Dr. K. Parish Venkata Kumar. (2020). Study and Analysis of Different Data Mining Algorithms Classification for Fault Detection. International Journal of Advanced Science and Technology, 29(06), 3576 - 3583. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/14159