Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis

Jiang, Qingchao; Ding, Steven X.; Wang, Yang; Yan, Xuefeng

doi:10.1109/tie.2017.2698422

Cited by 104 publications

(56 citation statements)

References 37 publications

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“…From (10), it is noticed that the reformulated optimization problem without penalties will reduce to that of KPCA. As can be seen in (8) and 10, there is no approximation and the excessive computation of K 1 2 is avoided. The features χ cannot be expressed with an explicit function.…”

Section: A Optimization Problem Based On Elastic Net Regularizationmentioning

confidence: 99%

“…Due to the rapid development in the fields of storage and sensor technologies, data-driven PM-FD methods have gained a substantial amount of research attention in recent years [3]- [5]. As the representative data-driven PM-FD approaches, principal component analysis (PCA), canonical correlative analysis (CCA), partial least squares (PLS), have been widely applied in various industrial processes [6]- [8]. For example, Zhou et al [9] developed a The associate editor coordinating the review of this manuscript and approving it for publication was Francesco Tedesco.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

Guo

Gao

et al. 2019

IEEE Access

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Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high-computation cost. To address this problem, obtaining sparse coefficients in KPCA is of paramount importance, particularly for real-time process monitoring and large-scale processes. In this paper, a new sparse kernel principal component analysis via sequential approach, named SSKPCA is proposed for nonlinear process monitoring. We first incorporate elastic net regularization into the framework of KPCA to establish a modified optimization problem. Then, a sequential approach is employed to derive the solution. Different from the existing sparse KPCA method based on elastic net regularization, the extra computations associated with the kernel matrix, such as matrix inversion and matrix square root are avoided in the optimizing procedure for solving the modified optimization problem. Therefore, the proposed SSKPCA method is more efficient in numerical implementation. The SSKPCA-based T 2 and squared prediction error (Q) statistics are constructed for fault detection. Furthermore, the sensitivity analysis principle is adopted for fault identification. A comparative study of Tennessee Eastman Process (TEP) is carried out to illustrate the ability and efficiency of the proposed SSKPCA-based nonlinear process monitoring method. INDEX TERMS Process monitoring, kernel-based methods, principal component analysis, sparsity, sequential approach.

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Section: A Optimization Problem Based On Elastic Net Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

Guo

Gao

et al. 2019

IEEE Access

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“…In modern industrial processes, it is important to guarantee process safety, achieve energy conservation, and improve product quality, all of which often largely depend on the timely monitoring and control of important quality variables . However, due to reasons like harsh measurement environment, cost of expensive instruments, and significant measurement delays, most of these quality variables are often difficult to measure online by hard sensors in actual production processes.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

Yuan

Wang

et al. 2019

Can J Chem Eng

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Industrial processes are often characterized with high nonlinearities and dynamics.For soft sensor modelling, it is important to model the nonlinear and dynamic relationship between input and output data. Thus, long short-term memory (LSTM) networks are suitable for quality prediction of soft sensor modelling. However, they do not consider the relevance of different input variables with the quality variable. To address this issue, a variable attention-based long short-term memory (VA-LSTM) network is proposed for soft sensing in this paper. In VA-LSTM, variable attention is designed to identify important input variables according to their relevance with quality prediction. After that, different attention weights are calculated and assigned to further obtain a weighted input sample at each time step.Finally, the LSTM network is exploited to capture the long-term dependencies of the weighted input time series to predict the quality variable. The performance of the proposed modelling method is validated on an industrial debutanizer column and a hydrocracking process. K E Y W O R D Sattention, deep learning, long short-term memory, prediction, soft sensor

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“…23 In recent years, many monitoring algorithms based on data-driven subspace segmentation have been published. [24][25][26][27] These methods can aid in decreasing process complexity and focus on local data characteristics to reduce the risk of variability being overwhelmed under abnormal conditions and improve monitoring performance. On the basis of the PCA, an approach for monitoring nonlinear processes with multiple linear subspaces is established.…”

Section: Introductionmentioning

confidence: 99%

Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Yan

Jiang

et al. 2019

Journal of Chemometrics

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Batch process data are time‐varying dynamic and non‐Gaussian distributed. In addition, for multivariate statistical process monitoring, their variability can be overwhelmed when considering local variability behavior. To address the abovementioned issues, an improved batch process monitoring approach is presented that integrates just‐in‐time learning and multiple subspace support vector data description (JITL‐MSSVDD). A new multiple subspace segmentation method is proposed that classifies a contribution array that is calculated on the mixing matrix of independent component analysis (ICA). Offline, the variable subspace segmentation rule can be obtained from the proposed method. The subspace monitoring models can reduce the risk of the variability being overwhelmed. Online, local modeling samples are collected through JITL, which can reduce the impact of the time‐varying dynamic on modeling accuracy. Then, in accordance with the variable subspace segmentation rule, which is obtained offline, MSSVDD models are constructed to solve non‐Gaussian problems. The advantage of the proposed JITL‐MSSVDD is demonstrated through the standard test model fed‐batch penicillin fermentation process.

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Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis

Cited by 104 publications

References 37 publications

Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

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