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首页> 外文期刊>IEEE transactions on industrial informatics >A Unified Probabilistic Monitoring Framework for Multimode Processes Based on Probabilistic Linear Discriminant Analysis
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A Unified Probabilistic Monitoring Framework for Multimode Processes Based on Probabilistic Linear Discriminant Analysis

机译：基于概率线性判别分析的多模过程统一概率监测框架

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摘要

This article develops a novel probabilistic monitoring framework for industrial processes with multiple operational conditions. The proposed method is based on the probabilistic linear discriminant analysis (PLDA), which relies on two sets of latent variables, i.e., the between-class and within-class latent variables. In order to deal with the large within-class variations in multi-mode industrial processes, this approach modifies the original PLDA by introducing a separate within-class loading matrix for each operational mode and designs an expectation maximization (EM) algorithm to estimate the model parameters from the training samples. Mode identification for test samples is achieved by investigating the cosine similarity in the between-class latent variables and two monitoring statistics corresponding to within-class latent variables and the residuals are considered for fault detection. To diagnose the process fault, this article further develops a sparse probabilistic generative model based on PLDA for fault isolation. The enhanced performance of the proposed method is illustrated by applications to numerical examples and industrial processes.
机译：本文为具有多种操作条件的工业流程开发了一种新颖的概率监测框架。所提出的方法基于概率的线性判别分析（PLDA），其依赖于两组潜在变量，即，类之间和类之间的潜在变量。为了处理多模式工业过程中的大型课程变体，这种方法通过为每个操作模式引入单独的类加载矩阵来修改原始PLDA，并设计期望最大化（EM）算法来估算模型来自训练样本的参数。通过调查类潜变量中的余弦相似性和对应于类潜伏变量的两个监测统计来实现测试样本的模式识别，并且考虑了残留物的故障检测。为了诊断过程故障，本文进一步开发了基于PLDA进行故障隔离的稀疏概率生成模型。通过应用于数值例子和工业过程的应用，将提高所提出的方法的增强性能。

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