Forecasting Appliances Failure Using WEKA Classification Algorithms

  • Apinaya Prethi K.N, Dr.M.Sangeetha, Nithya.S

Abstract

Domestic air conditioners normally used ondaily basis are gradually prone to failure due to various reasons. Usually, the end user is the only actor to identify poor performance of these appliances. When the appliance does not work anymore, then it is less beneficial to the user.  The main objective is to describe an intelligent, low cost system, which monitors the behavior of domestic air conditioners. The study will analyze the collected data, detect possible faults, and report the possibility of a failure in the nearer future which helps to avoid extreme failure cases. The complete system analyses the dataset using machine learning algorithms like Decision tree, Logistic regression, Naïve Bayes, SVM to identify fault in advance, as well as, predict the need of preventive maintenance. The study experimented with WEKA; consequence shows that SVM algorithm gives more efficiency in less processing time.

Published
2021-11-12
How to Cite
Apinaya Prethi K.N, Dr.M.Sangeetha, Nithya.S. (2021). Forecasting Appliances Failure Using WEKA Classification Algorithms. Design Engineering, 11716 - 11722. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/6242
Section
Articles