Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

• Main Authors: Arantxa Contreras-Valdes, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Martin Valtierra-Rodriguez
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Applied Sciences
• Subjects: data classification; data mining; data regression; electric equipment; fault diagnosis
• Online Access: https://www.mdpi.com/2076-3417/10/3/950

Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.