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Shock and Vibration
Volume 2016 (2016), Article ID 8538165, 6 pages
Research Article

A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

1Department of Computer, Charmo University, Sulaymaniyah, Iraq
2Department of Software Engineering, Koya University, Erbil, Iraq
3Halabja Institution, Halabja, Iraq

Received 8 November 2015; Revised 12 January 2016; Accepted 17 January 2016

Academic Editor: Arturo Garcia-Perez

Copyright © 2016 Zrar Kh. Abdul et al. 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.


Gear fault detection is one of the underlying research areas in the field of condition monitoring of rotating machines. Many methods have been proposed as an approach. One of the major tasks to obtain the best fault detection is to examine what type of feature(s) should be taken out to clarify/improve the situation. In this paper, a new method is used to extract features from the vibration signal, called 1D local binary pattern (1D LBP). Vibration signals of a rotating machine with normal, break, and crack gears are processed for feature extraction. The extracted features from the original signals are utilized as inputs to a classifier based on -Nearest Neighbour (-NN) and Support Vector Machine (SVM) for three classes (normal, break, or crack). The effectiveness of the proposed approach is evaluated for gear fault detection, on the vibration data obtained from the Prognostic Health Monitoring (PHM’09) Data Challenge. The experiment results show that the 1D LBP method can extract the effective and relevant features for detecting fault in the gear. Moreover, we have adopted the LOSO and LOLO cross-validation approaches to investigate the effects of speed and load in fault detection.