贝叶斯层次图 神经网络与不确定性反可靠的故障诊断的工业过程
IEEE transactions on neural networks and learning systems
|October 16, 2023
概括
这项研究介绍了贝叶斯层次图神经网络 (BHGNN),用于可靠的工业故障诊断. 新的不确定性反机制增强了从不确定的数据中学习,提高了诊断准确性和可靠性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 深度学习 (DL) 在工业故障诊断方面表现出色,但在不值得信赖的点估计方面扎.
- 贝叶斯推理通过量化决策不确定性来提供可靠的故障诊断,但目前的方法并没有将不确定性纳入训练中.
- 现有的贝叶斯式DL方法缺乏利用不确定性信息提高故障诊断性能的机制.
研究的目的:
- 开发一种可靠的故障诊断方法,使用贝叶斯式DL框架.
- 解决在现有方法中在培训过程中不使用的不确定性信息的限制.
- 提高高度不确定性样本的学习能力,提高整体故障诊断性能.
主要方法:
- 提出一个贝叶斯层次图神经网络 (BHGNN) 具有不确定性反机制.
- 使用变化性脱落来捕捉认识和 aleatoric 不确定性.
- 模型过程数据作为一个层次图 (HG) 集成的交互意识模块和物理拓知识.
- 利用不确定性信息来调整时间一致性 (TC) 约束,以实现强大的特征学习.
主要成果:
- BHGNN在三相流量设施 (TFF) 和安全水处理 (SWaT) 数据集上的故障诊断中表现出卓越和竞争力的表现.
- 不确定性反机制有效地改善了从不确定的样本中学习.
- 拟议的方法成功地将数据与域名知识集成在一起,以增强故障表示.
结论:
- 通过有效量化和利用不确定性,BHGNN方法为故障诊断提供了可靠的方法.
- 通过层次图表集成领域知识显著增强了错误表示学习.
- 拟议的方法为工业过程中的智能故障诊断提供了强大而可靠的解决方案.
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