缺失数据下分布式过程监控的NVBPCA与CCA方法

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本文主要探讨了在大规模多单元过程监控中处理缺失数据的问题，传统的方法通常假设完整的过程测量数据是可用的，但在实际应用中，尤其是在大型系统中，缺失数据是一个常见的挑战。针对这一问题，作者提出了一种基于邻域变分贝叶斯多元分析（Neighborhood Variational Bayesian Multivariate Analysis, NVBPCA）与典型相关分析（Canonical Correlation Analysis, CCA）相结合的分布式过程监控方法。 NVBPCA的核心思想是利用局部和邻近单元的信息来重建缺失的观测值，强调了数据之间的空间依赖性。这种方法通过非参数贝叶斯模型对局部数据进行建模，同时考虑了邻居间的相似性和差异性，以提高数据完整性的恢复效果。在处理缺失数据时，NVBPCA能够有效地降低由于数据不完全导致的监测准确性下降。接着，文章引入了基于CCA的局部监控器，它利用来自本地单元以及邻近单元的数据，不仅能够识别当地单元的状态，还能确定检测到的故障类型。CCA在这里作为数据分析工具，可以揭示不同单元间潜在的相关性，使得分布式监控系统能够更准确地识别异常行为，即便在存在大量缺失数据的情况下。此外，论文还可能讨论了算法的分布式实现，考虑到网络通信和计算资源的限制，如何在多单元环境中有效地并行执行NVBPCA和CCA，以减少整体监控延迟并提高效率。可能包括了数据压缩、通信优化以及并行计算策略的设计。为了验证这种方法的有效性，作者可能提供了理论分析、仿真结果以及实际工业案例研究。这些结果展示了在缺失数据条件下，该方法相对于传统方法在监测性能、鲁棒性和适应性上的改进。最后，文章总结了研究的局限性，并提出了未来可能的研究方向，如进一步提高模型的实时性和扩展到其他复杂过程监控场景。这篇研究论文为分布式过程监控中的缺失数据问题提供了一种创新解决方案，结合了变分贝叶斯和典型相关分析，旨在提升在大规模多单元环境下的监测效率和准确性。通过实证分析和比较，它展示了在处理缺失数据时的有效性，具有重要的实际应用价值。

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 1

Neighborhood Variational Bayesian Multivariate

Analysis for Distributed Process Monitoring

With Missing Data

Qingchao Jiang , Member, IEEE, Xuefeng Yan , and Biao Huang , Fellow, IEEE

Abstract—Conventional methods for distributed monitoring

commonly assume that complete process measurements are avail-

able. However, the problem of missing data is often encountered

in the monitoring of large-scale multiunit processes. This paper

proposes an approach based on a neighborhood variational

Bayesian principal component analysis (NVBPCA) and canonical

correlation analysis (CCA) for the efﬁcient distributed monitoring

of multiunit processes in the presence of missing data. Missing

observations for a local unit are reconstructed through NVBPCA

by considering information from both local and neighboring

units. A CCA-based local monitor, which identiﬁes the status of

the local unit and the type of a detected fault using information

from both the local and neighboring units, is then developed.

The NVBPCA–CCA approach has a better performance since its

missing data handling and local monitor construction consider

information from both the local and neighboring units. The

efﬁciency of the proposed monitoring method is demonstrated

through its application in a numerical example and an industrial

tail gas treatment process.

Index Terms—Canonical correlation analysis (CCA), distrib-

uted process monitoring, fault detection, missing data, variational

Bayesian principal component analysis (VBPCA).

I. INTRODUCTION

AULT detection plays an important role in maintain-

ing plant safety [1]–[4]. Data-driven process monitoring

has become popular with the progression of data collection

and transmission techniques [5]–[9]. Currently, plant-wide

processes are generally of large scale and consist of multiple

operation units. Monitoring of such processes is necessary but

Manuscript received June 6, 2018; revised August 5, 2018; accepted

September 9, 2018. Manuscript received in ﬁnal form September 11, 2018.

This work was supported in part by the National Natural Science Foundation

of China under Grant 61603138, in part by the Shanghai Pujiang Pro-

gram under Grant 17PJD009, in part by the Fundamental Research Funds for

the Central Universities under Grant 222201717006 and Grant 222201714027,

in part by the Program of Introducing Talents of Discipline to Universities

(the 111 Project) under Grant B17017, and in part by the Natural Science and

Engineering Research Council of Canada. Recommended by Associate Editor

P. Mha sk ar. (Corresponding authors: Xuefeng Yan; Biao Huang.)

Q. Jiang and X. Yan are with the Key Laboratory of Advanced Control

and Optimization for Chemical Processes, Ministry of Education, East China

University of Science and Technology, Shanghai 200237, China (e-mail:

qchjiang@ecust.edu.cn; xfyan@ecust.edu.cn).

B. Huang is with the Department of Chemical and Materials Engi-

neering, University of Alberta, Edmonton, AB T6G 2V4, Canada (e-mail:

bhuang@ualberta.ca).

Color versions of one or more of the ﬁgures in this paper are available

online at http://ieeexplore.ieee.org.

Digital Object Identiﬁer 10.1109/TCST.2018.2870570

challenging [10], [11]. Multiblock and distributed monitoring

methods are of signiﬁcant interest in the monitoring of large-

scale multiunit processes [12].

A multiblock or distributed monitoring method decom-

poses a process into subblocks to reduce complexity and

enable the exploration of local process behaviors [10], [13].

A method based on multiblock partial least square (PLS)

and principal component analysis (PCA) is developed in [14].

In [15], several multiblock PCA and multiblock PLS methods

are compared with each other. In [16], multiblock meth-

ods are further analyzed and improved. Data-driven distrib-

uted monitoring methods have been developed recently. For

example, in [13], distributed PCA monitors are developed

on the basis of loading vectors. Mutual-information-based

distributed monitors are developed in [17]. A performance-

driven distributed monitoring method is proposed in [18],

and a distributed monitoring framework is presented in [12].

More recently, distributed monitoring methods with big data

have been developed [19], [20]. A parallel PCA-kernel PCA

method is developed in [21]. These multiblock or distributed

monitoring methods lay the foundation for large-scale process

monitoring. Nevertheless, the following deﬁciencies need to be

further addressed: ﬁrst, multiblock or distributed monitoring

methods do not elaborate on the monitoring of local units.

Second, multiblock or distributed monitoring methods do not

identify the type of a detected fault. Third, the missing data

problem encountered in the monitoring of large-scale multiunit

processes has not been addressed.

Canonical correlation analysis (CCA), a basic multivariate

analytical method, has promising applications in monitoring

large-scale multiunit processes. A CCA-based residual gener-

ation approach has been recently proposed in [22]. A CCA

approach integrated with a randomized algorithm for non-

Gaussian fault detection is presented in [23]. In [24], a multiset

CCA is used to extract the joint feature and individual features

of multiunit processes. This approach is further extended to

parallel running batch processes in [25]. In [26], a CCA-

based distributed monitoring approach for large-scale multiunit

processes is proposed. In [27], a time-slice CCA is proposed

for key unit monitoring of batch processes, in which the type

of a detected fault, i.e., relevant or irrelevant with other units,

is identiﬁed. However, the following deﬁciencies exist in the

CCA-based methods: ﬁrst, the common missing data problem

is not discussed. The CCA-based monitoring focuses much

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