| 张守京， 郭文飞， 张荣川.基于BP神经网络的缝纫工序状态识别[J].轻工机械,2021,39(2):82-87 |
| 基于BP神经网络的缝纫工序状态识别 |
| State Identification of Sewing Procedure Based on BP Neural Network |
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| DOI：10.3969/j.issn.1005 2895.2021.02.015 |
| 中文关键词: 平缝机 伺服驱动电流 BP神经网络 缝纫工序 |
| 英文关键词:flat sewing machine servo drive current BP neural network sewing procedure |
| 基金项目:国家重点研发计划项目（2019YFB1707205）；陕西省教育厅科研计划项目（17JK0321）；西安市现代智能纺织装备重点实验室（2019220614SYS021CG043）。 |
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| 中文摘要: |
| 为了在缝纫过程中对产品进行监测识别，提高缝纫产品质量，课题组提出了一种基于 BP神经网络的缝纫工序状态识别方法。首先采集正常工况下缝纫机伺服电机驱动电流信号，将采集到的信号进行时域、频域分析，从中提取出缝纫工序状态相关性较好的几个特征量，将其组成敏感特征向量；然后搭建BP神经网络进行训练学习来识别多种未知工序状态。实验表明该方法能够准确识别分类缝纫工序状态，实现了远程工作状态监测。 |
| 英文摘要: |
| In order to realize the monitoring and identification of sewing process and improve the quality of sewing product, a sewing process status identification method based on BP neural network was proposed. First, the driving current signals of the sewing machine servo motor under normal working conditions are collected. Firstly, the driving current signals of the sewing machine servo motor under normal working condition were collected, and the collected signal were analyzed in time domain and frequency domain. Several characteristic quantities with good correlation of sewing process state were extracted to form sensitive feature vectors and then the BP neural network was set up to carry on the training study to identify a variety of unknown process conditions. The experiment proves that the method can accurately identify and classify the sewing process status and realize the remote working status monitoring. |
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