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Automating plastic bearing fault diagnosisusing continuous wavelet transform and neural networks |
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| รหัสดีโอไอ | |
| Creator | 1. Saipul Azmi Mohd Hashim 2. N. Abdul Razak 3. N. S. Sholahuddin |
| Title | Automating plastic bearing fault diagnosisusing continuous wavelet transform and neural networks |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2564 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 43 |
| Journal No. | 5 |
| Page no. | 1482-1490 |
| Keyword | bearing fault diagnosis, continuous wavelet transform, exponential moving average, neural network, principle component analysis |
| URL Website | https://rdo.psu.ac.th/sjst/index.php |
| ISSN | 0125-3395 |
| Abstract | Faulty plastic bearing is an initial alarm for bearing failure that can cause massive losses in the production line. Thelosses include restarting of production, producing of defective products, and even human casualty. This paper therefore aims topropose an automated plastic bearing fault detection and classification system. The system begins by transforming the bearingvibration signals into coefficients with continuous wavelet transform. The coefficients are then filtered by coefficients reductionand smoothing thereafter. Then, the filtered coefficients are classified by two ANN classifiers i.e. feed forward backpropagation(FFB) and recurrent neural network (RNN). The performance of both classifiers are finally measured and compared. The bestoverall performance is 90% detection rate by FFB. This system prevents bearing failure by giving an early alarm for faultsdetection and making the corrective action easier. Also, the single stage data processing and single signal type increase the dataprocessing efficiency. |
