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ROLLER BEARING FAULT DETECTION USING EMPIRICAL MODE DECOMPOSITION AND ARTIFICIAL NEURAL NETWORK METHODS |
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| รหัสดีโอไอ | |
| Creator | Javad Zarekar |
| Title | ROLLER BEARING FAULT DETECTION USING EMPIRICAL MODE DECOMPOSITION AND ARTIFICIAL NEURAL NETWORK METHODS |
| Contributor | Mehrdad Nouri Khajavi, Gholamhassan Payganeh |
| Publisher | TUENGR Group |
| Publication Year | 2562 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 10 |
| Journal No. | 1 |
| Page no. | 99-109 |
| Keyword | ANN, EMD, EEMD, IMFs, HilbertHuang transform (HHT), Kurtosis coefficient non-stationary vibrations, time-frequency |
| URL Website | http://tuengr.com |
| Website title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| ISSN | 2228-9860 |
| Abstract | One of the methods for detection faults in structural and mechanical systems is processing vibrational signals extracted from the real system. The HilbertHuang transform (HHT) is a new and strong method for analyzing nonlinear and non-stationary vibr |
