锻压技术

基于稀疏自编码器与自组织映射网络的轧机颤振预警方法

英文标题：Rolling mill chatter warning method based on sparse auto-encoder and self-organizing map network

单位：1. 燕山大学 2. 南昌工学院

分类号：O332; TH113

出版年,卷(期):页码：2023,48(1):171-178

摘要：

颤振是轧机生产过程中常见的问题之一, 严重影响轧机的生产效率。为实现轧机颤振状态的实时监测, 预防轧机发生颤振, 提出了一种轧机颤振预警方法。该方法利用稀疏自编码器对轧机的振动数据进行降维融合, 并且通过自组织映射网络构建能够准确地反应轧机振动趋势的特征指标; 同时, 以轧机正常运行状态的数据为基准, 通过3σ 准则设定合理有效的阈值。实验结果表明: 所构造的轧机振动趋势特征指标以及设定的报警阈值能够及时发现轧机振动趋势的变化, 并在振动达到峰值之前进行报警。最后, 将提出的SAE-SOM 模型与AE-SOM 模型进行比较, 结果表明, SAE-SOM 模型更加稳定且能够更早发现振动状态的异常变化。

Chatter is one of the common problems in the production process of rolling mill, which seriously affects the production efficiency of rolling mill. Therefore, in order to monitor the chatter state of rolling mill in real-time and prevent the happening of chatter of rolling mill, a chatter warning method of rolling mill was proposed, which used the sparse auto-encoder to reduce the dimension of rolling mill vibration data and constructed the characteristic index that could accurately reflect the vibration trend of rolling mill through the self-organizing map network. At the same time, based on the data of normal running state for rolling mill, a reasonable and effective threshold value was set by 3σ criterion. The experimental results show that the constructed characteristic index of the vibration trend for rolling mill and the set alarm threshold value can detect the change of the vibration trend for rolling mill in time and give an alarm before the vibration reaches the peak value. Finally, compared with the AE-SOM model, the results show that the SAE-SOM model is more stable and can detect the abnormal changes in the vibration state earlier.

基金项目：

国家自然科学基金资助项目(61973262); 河北省自然科学基金资助项目(E2019203146); 中央引导地方科技发展资金项目(216Z2102G); 江西省教育厅科学技术研究项目(GJJ212504)

作者简介: 时培明(1979-), 男, 博士, 教授 E-mail: spm@ ysu. edu. cn

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