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预测汽车发动机制造质量与多传感器数据的制造组装工艺的预测.

Xinyu Yang1, Qianxi Zhang1, Junjie Bao1

  • 1School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.

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概括
此摘要是机器生成的。

这项研究引入了用于汽车发动机质量控制的边缘部署框架,通过使用先进的人工智能技术对杂,不平衡的传感器数据进行缺陷检测和性能预测,显著改善了该框架.

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发动机制造质量预测预测特性提取 特性提取不同质的多传感器数据.工业物联网工业物联网工业物联网传感器数据融合传感器数据融合

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科学领域:

  • 制造业 工程 制造工程
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 汽车发动机质量控制面临着高维度,噪音和不平衡的多传感器数据的挑战.
  • 现有的方法很难有效地处理复杂的制造数据用于诊断和预测.

研究的目的:

  • 开发一个边缘部署的框架,以提高汽车发动机质量控制中的诊断和预测能力.
  • 解决数据挑战,包括制造传感器数据中的高维度,噪音和阶级不平衡.

主要方法:

  • 使用Sparse自动编码器 (SAE) 来减少维度,并从超过12,000个制造参数中过噪声.
  • 采用特定类权重组合 (CSWE) 来减轻缺陷分类中的类不平衡.
  • 实施了适应性调节回归 (ARSR) 进行无监督的过渡性性能跟踪和预测.

主要成果:

  • 该框架实现了80±3 ms的超低推理延迟.
  • 由于有效处理类不平衡,故障拦截回忆能力提高了7.72%.
  • 通过对专家模型进行动态加权,相对预测误差减少了12%.
  • 在物理H4发动机组装线上,实际上将发动机重工率降低了7.2%.

结论:

  • 拟议的框架有效地解决了汽车发动机质量控制中的多传感器数据的挑战.
  • 边缘部署的解决方案在诊断准确性,预测性能和运营效率方面提供了显著的改进.
  • 通过各种数据集和物理装配线进行验证,证明了实际适用性和大幅降低重制率.