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深度概率主要组件分析用于过程监控

Xiangyin Kong, Yimeng He, Zhihuan Song

IEEE transactions on neural networks and learning systems

|April 23, 2024

概括

本研究介绍了一种深度概率主要组件分析 (DePPCA) 模型,用于高效的工业过程监控. DePPCA通过提取高级功能来实现准确的故障检测,从而实现快速有效的在线监控.

科学领域:

工业过程监控工业过程监控
机器学习机器学习
故障检测检测故障检测

背景情况:

像PPCA这样的概率潜变量模型 (PLVM) 对于工业过程监控至关重要.
现有的方法可能缺乏复杂工业数据所需的特征提取能力.

研究的目的:

提出一个新的深度概率主要组件分析 (DePPCA) 模型.
通过使用深度学习和概率模型来增强过程监控和故障检测.

主要方法:

构建DePPCA涉及到贪的层级预训练和端到端的微调.
使用级联 PPCA 模块进行层次深层结构提取.
通过变异推理进行理论验证.

主要成果:

即使使用单变特征压缩,DePPCA也可以实现卓越的监控性能.
该模型允许快速提取功能和在线监控程序.
在田纳西伊斯曼 (TE) 和多相流 (MPF) 过程中证明了有效性.

结论:

DePPCA提供了一个准确而有效的工业过程监控方法.
该模型集成了深度学习和概率建模,用于先进的故障检测.
拟议的方法允许使用最小的提取特征进行快速有效的监测.

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