DLCNN:一种深度逻辑卷积网络,用于船到岸起重机上升降机械的可解释故障诊断
IEEE transactions on neural networks and learning systems
|August 14, 2025
概括
这项研究引入了深度逻辑卷积神经网络 (DLCNN),用于在船到岸起重机 (STSC) 升降机械中可靠的故障诊断. DLCNN提高了可解释性,为关键港口操作中的故障识别提供了可靠的解释.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 海上运营 海上运营
背景情况:
- 船到岸起重机 (STSC) 起重机制的故障诊断对于港口的效率和安全至关重要.
- 现有的故障诊断深度学习模型缺乏透明度,阻碍了信任和可解释性.
研究的目的:
- 开发一个可解释的深度学习模型用于STSC升降机械故障诊断.
- 提高深度神经网络在工业应用中的可靠性.
主要方法:
- 介绍一个深度逻辑卷积神经网络 (DLCNN),集成符号推理.
- 从逻辑卷曲 (LC) 中提取信任规则,重点关注信息丢失,可解释性权衡和定量推理.
- 从完全连接的层中提取分类规则,以澄清特征标签关系.
主要成果:
- DLCNN在STSC升降机制的故障识别准确性方面表现出很高的表现.
- 该模型提供了增强的可解释性,可视化了卷积神经网络 (CNN) 的决策过程.
- 在STSC测试平台上的实验验证证证了该模型的有效性和实际工程价值.
结论:
- 开发的DLCNN为诊断STSC提升机制故障提供了强大且可解释的解决方案.
- 这种方法解决了工业故障诊断深度学习中的可解释性差距.
- 在港口环境中,DLCNN具有提高安全性和运营可靠性的巨大潜力.
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