基于典型相关性分析的过程监控系统

发布时间：2020-12-15 01:23

　　由于对生产质量体系性能和经济运行的要求越来越高,现代工业体系日益庞大,复杂性也越来越高。为了解决这些问题,数据驱动技术,如主成分分析（PCA）、偏最小二乘（PLS）和典型相关分析（CCA）用于系统故障诊断和过程监控。它们假设要研究的数据不是自相关的。然而,大多数大型化学工业工厂本质上都是非线性的,所以这些技术不能试用于它们,本质上是无效的。为了弥补这个缺陷,需要开发一种能够管理这些过程异常的算法。工业产品的需求正在迅速增长,因此提出了不同的适应性技术。典型相关分析（CCA）是多元数据驱动的方法,它将同时考虑输入输出过程数据。本文讨论了数据驱动技术的实现,如用于田纳西伊士曼（TE）过程监控的主成分分析（PCA）偏最小二乘（PLS）和典型相关分析。主成分分析（PCA）是用于检测和诊断化学过程中的故障的最常用的降维技术。尽管PCA在故障检测方面具有一定的最优性,并且已被广泛应用于故障诊断,但它不是最适合的用于故障诊断。与PCA和PLS相比,典型相关分析（CCA）已被证明可改善化学过程中的故障诊断。使用T²统计和Q统计（SPE）同时检测多个故障。在这项工作中,我们比较了这... 

【文章来源】：哈尔滨工业大学黑龙江省 211工程院校 985工程院校

【文章页数】：68 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
Chapter 1 Introduction
    1.1 Research background
    1.2 Source of the project
    1.3 Literature review
        1.3.1 Principal component analysis
        1.3.2 Partial Least Square
        1.3.3 Canonical Correlation Analysis
    1.4 Literature analysis
    1.5 Main content of research
        1.5.1 Introduction
        1.5.2 The canonical correlation Analysis
        1.5.3 Comparison techniques with CCA
        1.5.4 Industrial Benchmark
        1.5.5 Results
    1.6 Outline of thesis
    1.7 Concluding Remarks
Chapter 2 Basics of Fault Diagnosis
    2.1 Data-Driven based fault diagnosis
        2.1.1 Signal processing fault diagnosis
        2.1.2 Hardware redundancy fault diagnosis
        2.1.3 Plausibility test
        2.1.4 Model-based fault diagnosis
    2.2 Information infrastructure
        2.2.1 Data acquisition
        2.2.2 Networking
        2.2.3 High computation power availability
        2.2.4 The storage capacity
        2.2.5 Fundamentals for data-driven techniques requirement
    2.3 Process monitoring procedures
    2.4 Process monitoring measures
        2.4.1 Data-driven
        2.4.2 Analytical approach
        2.4.3 Knowledge base
    2.5 Process monitoring methods
        2.5.1 Parameter estimation
        2.5.2 Observers
        2.5.3 Parity relations
    2.6 Description of technical systems
    2.7 Basic principle of fault detection
    2.8 Basic statistical fault detection methods
    2.9 Test statistics
        2.9.1 Hotelling's T statistics
        2.9.2 Q statistics
    2.10 Concluding remarks
Chapter 3 Data-Driven Techniques
    3.1 Multivariate Statistics
        3.1.1 Data Pretreatment
        3.1.2 Outliers
    3.2 Univariate process monitoring
    3.3 Principal component analysis
        3.3.1 PCA based fault detection
        3.3.2 Fault Detection with PCA
    3.4 Partial Least Square
        3.4.1 Introduction
        3.4.2 Partial Least Square based fault diagnosis:
        3.4.3 Partial Least Square Algorithms
        3.4.4 PLS Fault detection algorithm
    3.5 Canonical Correlation Analysis
        3.5.1 Introduction
        3.5.2 The Basis of CCA Technique
        3.5.3 CCA based FD method
    3.6 Performance Evaluation
        3.6.1 False Alarm Rate
        3.6.2 Fault Detection Rate
    3.7 Numerical Study Example
    3.8 Concluding remarks
Chapter 4 Benchmark Case
    4.1 Case Study on TE Benchmark Process
    4.2 Background
    4.3 Process flow sheet
    4.4 Main Process Variables
    4.5 Simulated Faults in TEP
    4.6 Results and discussion
        4.6.1 Application to TEP
    4.7 Concluding remarks
Conclusions and Future work
References
Acknowledgement



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