A Process Monitoring Scheme for Uneven-Duration Batch Process Based on Sequential Moving Principal Component Analysis

Guo, Runxia; Zhang, Na

doi:10.1109/tcst.2018.2876140

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…Zhang et al 24 proposed a twodirectional concurrent strategy for multimode and multiphase batch process monitoring with the uneven problem. Guo and Zhang 25 proposed a novel subphase partition method under different conditions to solve the problem of uneven duration batches. However, batch runs not only need to have the same duration length but also should keep the key events that happened at the same time.…”

Section: Introductionmentioning

confidence: 99%

Online monitoring and fault diagnosis for uneven length batch process based on multi‐way orthogonal enhanced neighborhood preserving embedding

Zhang

Zhao

Hui

et al. 2022

Asia-Pacific J Chem Eng

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In the practical batch process, the duration of each batch is probably different, and the key event happened may also vary from batch to batch. This paper proposes an effective fault diagnosis algorithm to synchronize the batch runs with uneven duration and keep the key features that reflect the running state of batch process. To address the batch out of sync, the relaxed greedy time warping is used to accurate on-line synchronization of ongoing batch and avoid evaluating the optimal path every time a new sample is available. For online monitoring and fault diagnosis, an important step is how to extract key features that reflect the running state of batch process, on which basis a weight matrix considers the distance between the neighbor data is embedded to construct the enhanced objective function, and then the mutually orthogonal basis functions which represent the local geometrical structure are computed to extract key features of batch process. After a fault is detected, the contribution plot method is used to diagnose the variables that cause this fault. The effectiveness of the proposed algorithm is verified by the penicillin fermentation process.

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Section: Introductionmentioning

confidence: 99%

Online monitoring and fault diagnosis for uneven length batch process based on multi‐way orthogonal enhanced neighborhood preserving embedding

Zhang

Zhao

Hui

et al. 2022

Asia-Pacific J Chem Eng

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show abstract

“…Both batch and continuous processes are important modes of production in modern industry. Recently, with the rapid development of process industry, the proportion of batch processes is growing due to the increasing demand of highvalue products such as pharmaceuticals, polymers and semiconductors [1][2][3][4][5][6]. In terms of the situation, researchers focus on the quality prediction and monitoring problem of batch processes to ensure the product quality and process safety.…”

Section: Introductionmentioning

confidence: 99%

Improved Mahalanobis Distance Based JITL-LSTM Soft Sensor for Multiphase Batch Processes

Zheng

Shen

2021

IEEE Access

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To predict key variables of complicated batch processes, the long short-term memory (LSTM) soft sensor is developed to deal with both data nonlinearity and dynamics. To extract proper historical samples and implement the real-time modeling scheme with model updating strategy, the just-in-time learning (JITL) algorithm is widely used at the data selection stage of LSTM soft sensor. However, the multiphase issue of batch processes are not considered for the conventional JITL-LSTM soft sensor. In this paper, a multiphase Mahalanobis distance based JITL framework is developed to integrate the phase recognition strategy into the similarity measurement and data selection scheme, by which an extra step of phase identification can be avoided and the accuracy of JITL can be significantly improved. Thus, batch samples from different operating phases can be recognized without an additional phase identification step. By the use of the Mahalanobis Distance based JITL-LSTM Soft Sensor, the probability of data mismatch can be significantly reduced so that the accuracy of quality prediction can be promoted. Two simulation cases are provided to verify the effectiveness of the proposed method consisting of a fed-batch reactor process and the penicillin fermentation process.

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“…With the considerable progress in scientific computing power, a huge amount of industrial process data can be fully utilized, and multivariable statistical process monitoring methods have achieved remarkable results (Ge, 2017; Li et al, 2020a; Marcos et al, 2019). Among these methods, the most studied are principal component analysis (Bounoua et al, 2020), Fisher discriminant analysis (Wu et al, 2020), independent component analysis (Wu et al, 2018), and their improved versions (Guo and Zhang, 2020; Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Stacked sparse autoencoders that preserve the local and global feature structures for fault detection

Yin

Yan

2021

Transactions of the Institute of Measurement and Control

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Although the model based on an autoencoder (AE) exhibits strong feature extraction capability without data labeling, such model is less likely to consider the structural distribution of the original data and the extracted feature is uninterpretable. In this study, a new stacked sparse AE (SSAE) based on the preservation of local and global feature structures is proposed for fault detection. Two additional loss terms are included in the loss function of SSAE to retain the local and global structures of the original data. The preservation of the local feature considers the nearest neighbor of data in space, while that of the global feature considers the variance information of data. The final feature is not only a deep representation of data, but it also retains structural information as much as possible. The proposed model demonstrates remarkable detection performance in case studies of a numerical process and the Tennessee Eastman process.

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A Process Monitoring Scheme for Uneven-Duration Batch Process Based on Sequential Moving Principal Component Analysis

Cited by 16 publications

References 24 publications

Online monitoring and fault diagnosis for uneven length batch process based on multi‐way orthogonal enhanced neighborhood preserving embedding

Online monitoring and fault diagnosis for uneven length batch process based on multi‐way orthogonal enhanced neighborhood preserving embedding

Improved Mahalanobis Distance Based JITL-LSTM Soft Sensor for Multiphase Batch Processes

Stacked sparse autoencoders that preserve the local and global feature structures for fault detection

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