A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection
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/impro...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/8538165 |
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| _version_ | 1849401769903259648 |
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| author | Zrar Kh. Abdul Abdulbasit Al-Talabani Ayub O. Abdulrahman |
| author_facet | Zrar Kh. Abdul Abdulbasit Al-Talabani Ayub O. Abdulrahman |
| author_sort | Zrar Kh. Abdul |
| collection | DOAJ |
| description | 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 k-Nearest Neighbour (k-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. |
| format | Article |
| id | doaj-art-847bd539aaa240b4b73abf2eb359080a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-847bd539aaa240b4b73abf2eb359080a2025-08-20T03:37:42ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/85381658538165A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault DetectionZrar Kh. Abdul0Abdulbasit Al-Talabani1Ayub O. Abdulrahman2Department of Computer, Charmo University, Sulaymaniyah, IraqDepartment of Software Engineering, Koya University, Erbil, IraqHalabja Institution, Halabja, IraqGear 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 k-Nearest Neighbour (k-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.http://dx.doi.org/10.1155/2016/8538165 |
| spellingShingle | Zrar Kh. Abdul Abdulbasit Al-Talabani Ayub O. Abdulrahman A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection Shock and Vibration |
| title | A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection |
| title_full | A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection |
| title_fullStr | A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection |
| title_full_unstemmed | A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection |
| title_short | A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection |
| title_sort | new feature extraction technique based on 1d local binary pattern for gear fault detection |
| url | http://dx.doi.org/10.1155/2016/8538165 |
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