一个模具损坏监测算法,用于电力金成型机使用双向长短期内存在物联网平台上的物联网平台
Hao-Pu Lin1, Yuan-Chieh Chen2, Chin-Chuan Han2
1Ph. D. Program in Material and Chemical Engineering, National United University, MiaoLi 360302, Taiwan.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种新的算法,用于使用振动数据评估模具健康状况. 该系统有效地预测模具的振动,使得早期检测损坏和增强预测性维护.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 模具健康评估对于制造效率至关重要.
- 传统方法可能无法及时检测菌退化.
- 振动分析提供了一个有前途的非侵入性监测方法.
研究的目的:
- 通过使用振动数据,提出一种分析和监测算法来评估模具健康状况.
- 开发一种用于早期检测菌损伤的预测模型.
- 实施预测性维护的早期预警系统.
主要方法:
- 使用惯性测量单元 (IMU) 和嵌入式系统获取振动数据.
- 通过物联网 (IoT) 平台通过消息队列远程测量传输 (MQTT) 协议收集数据.
- 训练双向长期短期记忆 (Bi-LSTM) 模型,使用注意力机制来预测振动.
- 计算平均平方误差 (MSE) 来评估菌的健康状况.
主要成果:
- 该Bi-LSTM模型准确地预测了正常的冲压振动.
- 在正常 (<0.5) 和异常 (>1.0) 模具条件下观察到明显的MSE值.
- 该系统在通知运营商潜在的模具问题方面表现出有效性.
结论:
- 开发的算法和预测模型可以有效评估菌健康状况.
- 通过使用振动数据,成功实施了模具损伤的早期预警系统.
- 这种方法显著提高了制造业的预测性维护能力.
相关概念视频
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This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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In this model, each generator is connected to a...
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