| 引用本文: | 刘著文,王小明,杨志强,刘鑫,师亚珊,张帅,王海宇,李超.基于FMEA和LS–SVM的卷烟卷接过程质量监测与诊断[J].包装工程,2023,44(3):255-260. |
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| 摘要: |
| 目的 针对烟支卷接过程质量监测精度低和效率差的问题，提出一种基于最小二乘支持向量机的卷接过程质量监控潜在失效模式及影响分析方法。方法 首先采用FMEA技术对卷接过程潜在失效模式进行识别和措施优先级判定。其次，通过关联度分析法得到关键失效模式的特征信号。最后，利用LS–SVM分类模型构建过程质量监测与诊断模型。通过实际生产数据对所提方法的性能进行验证。结果 对7种不同失效模式的识别，文中所提方法的总体平均识别精度达到93.53%，在识别准确性和识别效率上显著优于BPNN和SVM诊断方法，为卷烟制造过程诊断提供了新的思路。 |
| 关键词: 卷接过程 质量诊断 失效模式及影响分析 最小二乘支持向量机 |
| DOI：10.19554/j.cnki.1001-3563.2023.03.032 |
| 分类号:TS433 |
| 基金项目:国家自然科学基金(71672209);河南中烟工业有限责任公司科技项目(XC202036) |
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| Quality Detection and Diagnosis of Cigarette Rolling Process Based on FMEA and LS-SVM |
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LIU Zhu-weng1, WANG Xiao-ming1, YANG Zhi-qiang1, LIU Xin1, SHI Ya-shan1, ZHANG Shuai2, WANG Hai-yu3, LI Chao4
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(1. China Tobacco Henan Industrial Co., Ltd., Zhengzhou 450000, China;2. School of Management Engineering, Henan University of Engineering, Zhengzhou 451191, China;3. School of Business, Zhengzhou University, Zhengzhou 450001, China;4. Henan Center Line Electronic Science and Technology Co., Ltd., Zhengzhou 450004, China)
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| Abstract: |
| The work aims to propose a potential failure mode and effects analysis method of quality detection in cigarette rolling process based on LS-SVM, so as to solve the problem of low accuracy and efficiency of quality detection in the cigarette rolling process. First of all, FMEA was used to identify the potential failure modes of rolling process and determine the priority of solutions. Secondly, the characteristic signals of key failure modes were obtained by correlation analysis. Finally, the LS-SVM classification model was used to construct process quality detection and diagnosis model. The performance of the proposed method was verified by actual production data. For the identification of seven different failure modes, the overall average identification accuracy of the proposed method was 93.53%, which was much better than BPNN and SVM models in identification accuracy and efficiency and provided a new way for diagnosis of cigarette rolling process. |
| Key words: rolling process quality diagnosis failure mode and effects analysis least squares-support vector machine |
