| Autor: |
Shuai Hou, Xiaolin Han, Xinyuan Zhang, Meijuan Bai, Fuan Hua |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 8, Pp 194685-194699 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2020.3030271 |
| Popis: |
The strip floating height is a key factor affecting production efficiency and quality of the air cushion furnace. At present, the air cushion furnace with hybrid nozzles is the most typical heating and drying equipment. In order to predict the floating height of the strip in this equipment, a hard-division and multi-model based floating height prediction method is proposed. In hard division method, the process is divided into several stable and vibration stages. On the one hand, time interval based novel density clustering algorithm is proposed so as to avoid the wrong division caused by noise at hard division stage. On the other hand, covariance matrices based local density is presented to better capture dynamic characteristics of the process. In the multi-model stage, a hybrid prediction model under stable state and XGBoost-based model under vibration state are established to predict the strip floating height. The hybrid prediction model is composed of mechanism model and data driven model. The mechanism model is proposed by combining the force balance equation and inviscid theory which can predict the major information of the floating height and the data driven model compensates the error of the mechanism model. A mount of experiments have been carried out on the existing air cushion experimental platform and show desirable prediction effect. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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