分数级神经网络用于检测光纤电缆制造过程中的过程偏差
Zbigniew Gomolka1, Ewa Zeslawska2, Lukasz Olbrot3
1Faculty of Exact and Technical Sciences, Institute of Computer Science, University of Rzeszow, 16C Tadeusza Rejtana Avenue, 35-959, Rzeszow, Poland. zgomolka@ur.edu.pl.
Scientific reports
|January 30, 2026
概括
这项研究引入了一种新的微分衍生长短期记忆 (FD-LSTM) 模型,用于光纤电缆制造中的异常检测. 先进的模型显著改善了微妙的过程偏差的识别,提高了产品质量和降低了成本.
科学领域:
- 工业制造业 工业制造业 工业制造业
- 数据科学数据科学数据科学
- 材料科学 材料科学 材料科学
背景情况:
- 异常检测对于降低成本和提高工业制造业的质量至关重要.
- 光纤电缆生产对影响光学性能的小参数偏差很敏感.
- 高维数据和缺乏标记异常需要无监督学习方法.
研究的目的:
- 开发一种先进的无监督学习模型,用于光纤电缆制造中的异常检测.
- 在循环神经网络中利用微积分计算,以改进时间依赖模型.
- 提高复杂制造过程中检测微妙异常的准确性和能力.
主要方法:
- 提出了一个微分衍生式长期短期存储器 (FD-LSTM) 网络模型.
- 通过Grünwald-Letnikov方法实现了分数顺序导数.
- 应用无监督学习用于集群和标记高维数据中的生产异常.
主要成果:
- 在实际生产数据上,FD-LSTM模型实现了96.7%的准确性和0.93的F1得分.
- 在评估的数据集中实现了超过95%的预测性能.
- 与经典LSTM相比,证明了弱异常集群的可分离性提高和错误分类减少.
结论:
- FD-LSTM模型有效地将微积分计算集成到循环架构中,以进行强大的异常检测.
- 分数顺序导数增强了制造过程中复杂的时间动态的建模.
- 拟议的方法为工业环境中的质量控制提供了显著的进步.
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