Cross correlation-based blocking whitening slow feature analysis for coupled-data industrial process monitoring

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Jiao Meng, Xin Huo, Hewei Gao, Changchun He
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引用次数: 0

Abstract

Modern industrial systems are characterized by high complexity, strong coupling, and multi-source data interactions. Practical industrial process data are mostly nonlinear, dynamical, and manifested as temporal correlation, which makes it challenging for traditional monitoring methods to accurately capture the changes in internal state variables. To this end, a multi-block whitening slow feature analysis (MBW-SFA) approach is proposed in this paper, which utilizes the cross maximum information coefficient (CMIC) for nonlinear correlation analysis, blocking, as well as selective whitening transformation (SWT) for the nonlinear and strongly coupled process variables, so as to avoid the information loss caused by global whitening. The proposed MBW-SFA approach performs manifold mapping on the data with strong correlations, preserving key information while reducing dimensionality, and the selective whitening transformation is applied to prevent information loss across different variables. In addition, for scenarios involving partially known fault data, this study proposes a control limit optimization (CLO) function that balances fault identification and false alarms to calculate control limit thresholds based on slow feature monitoring statistics, achieving objective monitoring of industrial processes. The proposed approach is experimentally validated on Tennessee Eastman process, where the correlation between process variables is investigated, and the results show that the proposed method achieves excellent performance in process monitoring.
耦合数据工业过程监测中基于交叉相关的分块白化慢特征分析
现代工业系统具有高复杂性、强耦合性和多源数据交互的特点。实际工业过程数据大多是非线性的、动态的,表现为时间相关性,这使得传统的监测方法难以准确捕捉内部状态变量的变化。为此,本文提出了一种多块白化慢特征分析(MBW-SFA)方法,该方法利用交叉最大信息系数(CMIC)对非线性和强耦合过程变量进行非线性相关分析、分块和选择性白化变换(SWT),以避免全局白化造成的信息损失。提出的MBW-SFA方法对具有强相关性的数据进行流形映射,在降维的同时保留关键信息,并采用选择性白化变换防止不同变量之间的信息丢失。此外,对于故障数据部分已知的场景,本研究提出了一种控制极限优化(CLO)函数,该函数平衡故障识别和虚警,基于慢特征监测统计计算控制极限阈值,实现对工业过程的客观监测。在田纳西伊士曼过程中对该方法进行了实验验证,研究了过程变量之间的相关性,结果表明该方法在过程监控中取得了良好的效果。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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