| 李哲， 胡胜.基于多关联参数特征子空间的纺纱质量波动预测[J].轻工机械,2022,40(5):22-28 |
| 基于多关联参数特征子空间的纺纱质量波动预测 |
| Spinning Quality Fluctuation Prediction Based on Feature Subspace of Mul Correlation Parameters |
| |
| DOI：10.3969/j.issn.1005 2895.2022.05.004 |
| 中文关键词: 纺纱过程 质量波动预测 多关联参数 深度信念网络 特征子空间 受限玻尔兹曼机 |
| 英文关键词:spinning process quality fluctuation prediction multi correlation parameter DBN(Deep Believe Network) feature subspace RBM(Restricted Boltzmann Machine |
| 基金项目:中国纺织工业联合会指导计划项目（2020112）；陕西省自然科学基金（2022JQ 721）；西安工程大学博士启动基金项目 |
|
| 摘要点击次数: 336 |
| 全文下载次数: 280 |
| 中文摘要: |
| 针对纺纱生产过程影响因素多、监测维度广导致的过程波动难以分析和纱线质量难以预测的难题，课题组提出一种基于多关联参数特征子空间的纺纱质量波动预测方法。首先分析影响纱线质量的关联参数之间关系，构造能够表征纱线质量波动的特征子空间；然后构建面向特征子空间的纱线质量深度学习预测模型，实现纱线质量智能预测。通过实例进行分析，结果显示提出的方法能够有效分析纱线质量的多关联参数波动规律，并能准确对纱线质量进行预测。 |
| 英文摘要: |
| Aiming at the problems of the analysis process fluctuation and the prediction of yarn quality caused by multiple influencing factors and wide monitoring dimensions in the spinning production process, a method for predicting spinning quality fluctuations based on feature subspaces of multiple correlation parameters was proposed. Firstly, the relationship between the correlated parameters that affects yarn quality was analyzed, and the feature subspace that characterizes yarn quality fluctuations was constructed. Then the feature subspace oriented deep learning prediction model of yarn quality was constructed to realize the intelligent prediction of yarn quality. Finally, through the analysis of examples, the results show that the proposed method can effectively analyze the fluctuation law of multi correlation parameters of yarn quality and accurately predict yarn quality. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
