SCCAM:监督的对比卷积注意力机制,用于使用有限的故障样本进行前期可解释的故障诊断
IEEE transactions on neural networks and learning systems
|September 20, 2023
概括
这项研究引入了一种新的故障诊断方法,即监督对比卷积注意力机制 (SCCAM),它可以准确地识别工业过程故障及其根本原因,即使有有限的故障数据. 该SCCAM方法增强了分类,并提供可解释的特征级别解释.
科学领域:
- 工业过程监测和控制 工业过程监测和控制
- 机器学习用于故障诊断.
- 在工程领域的人工智能.
背景情况:
- 由于罕见的故障发生,工业故障诊断经常面临有限的故障样本的挑战.
- 现有的以注意力为基础的方法,如变压器,需要大量的数据,缺乏诱导偏差,导致有限样本的性能差.
- 目前的方法在数据稀缺的情况下,难以准确地分类故障并确定根本原因.
研究的目的:
- 开发一种有效的故障诊断方法,能够从有限的故障样本中学习.
- 解决工业过程中缺少故障数据的根本原因分析问题.
- 为故障诊断创建一个可解释的基于注意力的架构.
主要方法:
- 提出了一个监督的对比卷积注意力机制 (SCCAM),将卷积神经网络 (CNN) 与注意力机制集成在一起.
- 纳入监督对比学习 (SCL) 损失以增强对有限数据的分类能力.
- 使用卷积块注意模块 (CBAM) 进行预先解释和特征级解释.
主要成果:
- 在有限的故障样本下实现了准确的故障分类,超过了最先进的方法.
- 在不需要专家知识的情况下成功识别了故障的根本原因,并证明了预先的可解释性.
- 在各种故障场景中验证了SCCAM方法的连续动加热器 (CSTH) 和田纳西东曼 (TE) 工业过程基准.
结论:
- 在工业过程中,SCCAM方法有效地解决了用有限的样本进行故障诊断的挑战.
- 拟议的方法提供了准确的故障分类,并使直接根源原因分析,提高系统可靠性.
- 在可解释的机器学习中,SCCAM为工业故障诊断应用提供了显著的进步.
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