Development of an Estimative Model for Fault Detection in Gas Appliance Repair Services

Abstract

In this paper, we introduce a machine learning model to estimate the faulty parts of gas appliances from repair request information and customer data. Traditionally, technicians estimate which parts are faulty based on a customer request, preparing the necessary parts, and visiting the site. However, this approach relies heavily on the experience of the technicians and carries the risk of requiring a return visit. Additionally, a return visit may lead to a decrease in customer satisfaction. To address these challenges, we developed a machine learning model using gradient boosting trees and implemented an algorithm to aggregate necessary parts from past cases. Furthermore, we built a system to display these estimation results to the technicians and put it into operation. Experimental results and evaluations after system implementation suggested the effectiveness of the proposed method.