Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis

Xiu, Xianchao; Chen, Xiaolu; Kong, Lingchen; Liu, Wanquan

doi:10.1109/tcsii.2020.2988054

Cited by 33 publications

(12 citation statements)

References 17 publications

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“…The variables in this process contain two blocks of variables: the XMV block of 11 manipulated variables and the XMEAS block of 41 measured variables which include 22 process and 19 analysis variables. In this simulation, 22 process variables (XMEAS ) and 11 manipulated variables (XMV (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)) are chosen to be process input X, and select purge gas analysis component G (XMEAS (35)) as the quality output Y.…”

Section: Te Benchmarkmentioning

confidence: 99%

“…For example, Xiu et al 5 proposed a novel Laplacian regularized robust PCA method that can effectively capture the intrinsic non-linear geometric information. Other process-related fault detection methods have canonical correlation analysis (CCA), 6 non-negative matrix factorization (NMF), 7,8 and so on. Another particularly important research direction for MSPM is quality-related fault diagnosis, [9][10][11] in which qualityrelated fault detection and fault isolation are two key tasks.…”

Section: Introductionmentioning

confidence: 99%

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Quality-related fault diagnosis based onk-nearest neighbor rule for non-linear industrial processes

Ren

Tang

Zhang

2021

International Journal of Distributed Sensor Networks

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The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.

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Section: Te Benchmarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality-related fault diagnosis based onk-nearest neighbor rule for non-linear industrial processes

Ren

Tang

Zhang

2021

International Journal of Distributed Sensor Networks

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“…From the point of view of optimization, it is necessary to incorporate the data structure prior in the learning process. That is the main idea of the manifold learning method; see, e.g., [19], [20], [23], [24], [38], [44].…”

Section: Introductionmentioning

confidence: 99%

Manifold Constrained Low-Rank and Joint Sparse Learning for Dynamic Cardiac MRI

Meng

Xiu

2020

IEEE Access

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Reconstruction from highly accelerated dynamic magnetic resonance imaging (MRI) is of great significance for medical diagnosis. The application of low-rank and sparse matrix decomposition to MRI can improve imaging speed and efficiency. However, the consistence of the learned low-rank and sparse structures for similar input samples is not well addressed in literature. In this paper, we propose a manifold constrained low-rank and joint sparse learning model that embeds the manifold priors into lowrank and joint sparse decomposition. It is noted that the joint sparsity is investigated to exploit the shared information. Further, the manifold constraints for low-rank and joint sparse parts are forced the optimization process to satisfy the structure preservation requirement. To solve the above manifold learning problem, a manifold constrained alternating direction method of multipliers (McADMM) approach is designed. It is proved theoretically that the sequence generated by McADMM converges to a stationary point. Numerical comparisons on simulation data and real-world dynamic cardiac MRI data are presented to demonstrate its efficiency. INDEX TERMS Magnetic resonance imaging (MRI), dynamic cardiac MRI, low-rank and joint sparse decomposition, manifold learning, alternating direction method of multipliers (ADMM).

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“…The main idea is to make use of the offline data to train a general model, then apply the trained model to detect the online data [12]. Different from principal component analysis (PCA) [24,26] and partial least squares (PLS) [23,29], canonical correlation analysis (CCA) has shown its efficiency in exploring the input-output relationship of the process; see, e.g., [1,5,6,7,16,17,25]. The CCA is not a new multivariate statistical method, which can be tracked back to [14].…”

mentioning

confidence: 99%

“…It is well demonstrated that CCA can achieve higher detection performance than the classical PCA and PLS. After that, CCA-based FD has been widely recognized, such as non-Gaussian CCA [5], improved CCA [7], distributed CCA [16], multimode CCA [17], and sparse CCA [25], to name a few.…”

mentioning

confidence: 99%

A novel scheme for multivariate statistical fault detection with application to the Tennessee Eastman process

Xu¹,

Sun²,

Liu³

et al. 2021

MFC

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<p style='text-indent:20px;'>Canonical correlation analysis (CCA) has gained great success for fault detection (FD) in recent years. However, it cannot preserve the prior information of the underlying process. To cope with these difficulties, this paper proposes an improved CCA-based FD scheme using a novel multivariate statistical technique, called sparse collaborative regression (SCR). The core of the proposed method is to take the prior information as a supervisor, and then integrate it with CCA. Further, the <inline-formula><tex-math id="M1">\begin{document}$ \ell_{2,1} $\end{document}</tex-math></inline-formula>-norm is employed to reduce redundancy and avoid overfitting, which facilitates its interpretability. In order to solve the proposed SCR, an efficient alternating optimization algorithm is developed with convergence analysis. Finally, some experimental studies on a simulated example and the benchmark Tennessee Eastman process are conducted to demonstrate the superiority over the classical CCA in terms of the false alarm rate and fault detection rate. The detection results indicate that the proposed method is promising.</p>

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Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis

Cited by 33 publications

References 17 publications

Quality-related fault diagnosis based onk-nearest neighbor rule for non-linear industrial processes

Quality-related fault diagnosis based onk-nearest neighbor rule for non-linear industrial processes

Manifold Constrained Low-Rank and Joint Sparse Learning for Dynamic Cardiac MRI

A novel scheme for multivariate statistical fault detection with application to the Tennessee Eastman process

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