Survey on recursive statistical process monitoring methods

Wang, Youqing; Qin, Yihao; Lou, Zhijiang; Ma, Xin

doi:10.1002/cjce.24494

Cited by 6 publications

(4 citation statements)

References 70 publications

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Section: Introductionmentioning

confidence: 99%

“…The solid requirement of production safety and sustainable operation keeps motivating novel approaches for effectively monitoring the operating condition of modern industrial processes 1–4 . Nowadays, the great achievements in Industry 4.0 have been popularizing wider application of data‐driven process monitoring techniques 3–5 . Particularly, multivariate statistical process monitoring (MSPM) with a goal of extracting normal statistical signatures from a dataset given from the normal operating condition (NOC) has become a quite popular methodology for detecting possible faults or anomalies in industrial plants 4–6 .…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, the great achievements in Industry 4.0 have been popularizing wider application of data‐driven process monitoring techniques 3–5 . Particularly, multivariate statistical process monitoring (MSPM) with a goal of extracting normal statistical signatures from a dataset given from the normal operating condition (NOC) has become a quite popular methodology for detecting possible faults or anomalies in industrial plants 4–6 . On the basis of two fundamental analytical algorithms, that is, principal component analysis (PCA) and independent component analysis (ICA), 7–9 plenty of unsupervised modeling methods for representing the normal variation in the given dataset have been motivated 4–11 …”

Section: Introductionmentioning

confidence: 99%

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Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Hu,

Chang

2023

Journal of Chemometrics

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With a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just‐in‐time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly apply it for online monitored samples, the proposed JITLAR2G‐based approach provides a JITLAR2G model for the online monitored samples after data augmentation, so that the corresponding inconsistency within the given consecutive samples could be timely and adaptively uncovered. Instead of expressing the time‐serial relationship that generally accepted by the normal samples in the given dataset, solving the objective function designed for JITLAR2G in a just‐in‐time manner can adaptively and correspondingly seek but only one projecting vector as well as coefficient vector to generate residual, which points to the potential inconsistency inherited in the monitored samples, for the sole purpose of fault detection. As demonstrated through comparisons, the proposed JITLAR2G model can consistently guarantee its effectiveness, in terms of reducing both false alarm rate and missed alarm rate, for dynamic process monitoring, the salient performance achieved by the proposed JITLAR2G‐based method in contrast to the counterparts can be always confirmed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Hu,

Chang

2023

Journal of Chemometrics

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“…In response to these problems, process monitoring plays an increasingly critical role. [1][2][3][4][5] Three methods are often used: mechanism modelling, expert experience modelling, and data modelling.…”

mentioning

confidence: 99%

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

Wang

Tang

et al. 2023

Can J Chem Eng

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The overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block‐based attention mechanism stacked autoencoder (MB‐AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self‐attention is used to focus on the information of each block itself. Cross‐attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee‐Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB‐AMSAE algorithm in process monitoring.

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Adaptive Multimode Monitoring Based on Dictionary Learning with Online-Updating Window

Zheng,

Ma,

Cui

et al. 2023

2023 China Automation Congress (CAC)

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Survey on recursive statistical process monitoring methods

Cited by 6 publications

References 70 publications

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

Adaptive Multimode Monitoring Based on Dictionary Learning with Online-Updating Window

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