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Shock and Vibration
Volume 2018, Article ID 7460419, 10 pages
https://doi.org/10.1155/2018/7460419
Research Article

Vibration-Based Fault Diagnosis of Commutator Motor

AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics, Al. A. Mickiewicza 30, 30-059, Kraków, Poland

Correspondence should be addressed to Adam Glowacz; lp.ude.hga@wolgda

Received 15 July 2018; Revised 8 September 2018; Accepted 27 September 2018; Published 24 October 2018

Academic Editor: Tony Murmu

Copyright © 2018 Adam Glowacz and Witold Glowacz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (). The proposed methods have practical application in industries.