| Autor: |
Li Shengjia, Ma Yali, Zhao Yongsheng, Pu Dajun, Yan Shidang |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Jixie chuandong, Vol 47, Pp 78-85 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1004-2539 |
| DOI: |
10.16578/j.issn.1004.2539.2023.03.011 |
| Popis: |
Aiming at the difficulty of analyzing the meshing contact characteristics of gear transmission system under the big data formed by multiple working conditions and uncertain parameters, a meshing contact characteristics analysis method of gear transmission system based on data mining technology is proposed. Based on the principle of multi-dimensional Gaussian distribution and the finite element model of gear transmission system, the meshing contact characteristic data set of the system is constructed. The correlation between system parameters and meshing contact characteristics is analyzed using the maximum information coefficient, which provides a candidate feature subset for the prediction model. Then, the prediction model of meshing contact characteristics of the system is established using support vector machine and random forest algorithm, which realizes the efficient prediction of meshing contact characteristics of the system. The results show that the prediction error of the prediction model based on support vector machine is the smallest, and the average absolute percentage error is 3.87%, which is far less than the theoretical calculation error. Under the optimal feature subset, the prediction error index of contact characteristics of the prediction model based on support vector machine decreases significantly, and its average absolute percentage error reaches 3.03%, which is 21.71% lower than the prediction error of contact characteristics before optimization. These have verified the accuracy and effectiveness of the proposed method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
