Gearbox fault diagnosis using data fusion based on self-organizing map neural network
This article aims to provide an efficient fault diagnosis method for gearbox. A self-organizing map–based fault model is developed to provide effective diagnosis of the faults of gearboxes using the gear signals extracted from gearboxes operating with zero and three different types of faults. The ge...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2020-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720923476 |
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doaj-2c8a830beb4148d080773ffd44ca01402020-11-25T03:34:16ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772020-05-011610.1177/1550147720923476Gearbox fault diagnosis using data fusion based on self-organizing map neural networkZhang Qiang0Gu Jieying1Liu Junming2Tian Ying3Zhang Shilei4School of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaSchool of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaSchool of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaSchool of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaThis article aims to provide an efficient fault diagnosis method for gearbox. A self-organizing map–based fault model is developed to provide effective diagnosis of the faults of gearboxes using the gear signals extracted from gearboxes operating with zero and three different types of faults. The gear signals are collected by vibration and acoustic sensors, and pre-denoised using wavelet denoising and wavelet packet decomposition. The characteristic values are subsequently obtained using fast Fourier transform and infinite impulse response filtering. The results showed of the self-organizing map neural network diagnosis model can effectively diagnose gear fault information with a 95% diagnostic accuracy using four input characteristic values: (1) Y-axis vibration displacement amplitude, (2) Y-axis vibration acceleration amplitude, (3) acoustic emission energy amplitude, and (4) acoustic emission signal peak value. The proposed approach provides a novel method to more accurate diagnosis of gear fault pattern and improvement of working efficiency of mechanical instruments.https://doi.org/10.1177/1550147720923476 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Zhang Qiang Gu Jieying Liu Junming Tian Ying Zhang Shilei |
| spellingShingle |
Zhang Qiang Gu Jieying Liu Junming Tian Ying Zhang Shilei Gearbox fault diagnosis using data fusion based on self-organizing map neural network International Journal of Distributed Sensor Networks |
| author_facet |
Zhang Qiang Gu Jieying Liu Junming Tian Ying Zhang Shilei |
| author_sort |
Zhang Qiang |
| title |
Gearbox fault diagnosis using data fusion based on self-organizing map neural network |
| title_short |
Gearbox fault diagnosis using data fusion based on self-organizing map neural network |
| title_full |
Gearbox fault diagnosis using data fusion based on self-organizing map neural network |
| title_fullStr |
Gearbox fault diagnosis using data fusion based on self-organizing map neural network |
| title_full_unstemmed |
Gearbox fault diagnosis using data fusion based on self-organizing map neural network |
| title_sort |
gearbox fault diagnosis using data fusion based on self-organizing map neural network |
| publisher |
SAGE Publishing |
| series |
International Journal of Distributed Sensor Networks |
| issn |
1550-1477 |
| publishDate |
2020-05-01 |
| description |
This article aims to provide an efficient fault diagnosis method for gearbox. A self-organizing map–based fault model is developed to provide effective diagnosis of the faults of gearboxes using the gear signals extracted from gearboxes operating with zero and three different types of faults. The gear signals are collected by vibration and acoustic sensors, and pre-denoised using wavelet denoising and wavelet packet decomposition. The characteristic values are subsequently obtained using fast Fourier transform and infinite impulse response filtering. The results showed of the self-organizing map neural network diagnosis model can effectively diagnose gear fault information with a 95% diagnostic accuracy using four input characteristic values: (1) Y-axis vibration displacement amplitude, (2) Y-axis vibration acceleration amplitude, (3) acoustic emission energy amplitude, and (4) acoustic emission signal peak value. The proposed approach provides a novel method to more accurate diagnosis of gear fault pattern and improvement of working efficiency of mechanical instruments. |
| url |
https://doi.org/10.1177/1550147720923476 |
| work_keys_str_mv |
AT zhangqiang gearboxfaultdiagnosisusingdatafusionbasedonselforganizingmapneuralnetwork AT gujieying gearboxfaultdiagnosisusingdatafusionbasedonselforganizingmapneuralnetwork AT liujunming gearboxfaultdiagnosisusingdatafusionbasedonselforganizingmapneuralnetwork AT tianying gearboxfaultdiagnosisusingdatafusionbasedonselforganizingmapneuralnetwork AT zhangshilei gearboxfaultdiagnosisusingdatafusionbasedonselforganizingmapneuralnetwork |
| _version_ |
1724559626154803200 |