Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm

Zhao, Xiaoqiang; Liu, Kai; Hui, Yongyong

doi:10.1177/01423312221108519

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Batch processes have wide application in the production of products with high additional values such as pharmaceuticals, fine chemicals, and semiconductor material (Ji et al, 2023; Yoo et al, 2021; Zhao et al, 2023). Due to the extraordinary requirements for product quality, it is of great significance to precisely detect the value of quality variables in batch process (Liu et al, 2023; Mowbray et al, 2022; Tang and Li, 2020).…”

Section: Introductionmentioning

confidence: 99%

An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling

Zhu,

Xiong

2024

Transactions of the Institute of Measurement and Control

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For multiphase batch process, the characteristics of process data under various batches differ. Consequently, the soft sensor model built for a particular working condition is inapplicable to other working conditions. Besides, each batch can be divided into several phases whose characteristics are probably different. To address the above problems, a soft sensor model based on phase division and transfer learning strategy is proposed. First, transfer learning strategy is adopted to construct a soft sensor model applicable to various working conditions. Specifically, geodesic flow kernel based on linear local tangent space alignment (LLTSA-GFK) algorithm is designed. By projecting process data to the common manifold subspace, the distribution difference of data between various batches is reduced and the performance of the soft sensor model is enhanced. In addition, sequence-based fuzzy clustering and just-in-time learning (JITL) are adopted to solve the multistage characteristic for batch process. The root-mean-square error ( RMSE), coefficient of determination [Formula: see text], and mean absolute error ( MAE) are adopted to compare the conventional soft sensing approach (i.e., partial least-square regression based on JITL, support vector regression, and back propagation neural network) with the proposed approach. The superiority of the proposed model is verified by a fed-batch penicillin fermentation process.

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Section: Introductionmentioning

confidence: 99%

An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling

Zhu,

Xiong

2024

Transactions of the Institute of Measurement and Control

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“…Fault detection of batch process is an important research topic. [1][2][3][4][5] Traditional model-based methods have difficulty achieveing better performance of fault detection in complex batch processes, and data-driven methods are gaining more and more attention from scholars. As one of the important branches of data-driven approaches, multivariate statistical process monitoring (MSPM) methods have been successfully used for batch processes with satisfying results.…”

Section: Introductionmentioning

confidence: 99%

SiameseDeNPEnetwork framework for fault detection of batch process

Liu,

Zhao,

Mou

et al. 2023

Can J Chem Eng

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In batch processes, it is crucial to ensure safe production by fault detection. However, the long batch duration, limited runs, and strong nonlinearity of the data pose challenges. Incipient faults with small amplitudes further complicate the detection process. To achieve safe production, motivated by deep learning strategies, we propose a new fault detection method of batch process called Siamese deep neighbourhood preserving embedding network (SDeNPE). First, the DeNPE network is constructed by means of NPE and kernel functions, which utilizes the different types of kernel functions in the kernel mapping layer to extract diverse deep nonlinear features and overcome strong nonlinearity in the process data. Then, the Siamese network is used to obtain the different features between the data and improve the recognition of incipient faults. In addition, the deep extraction and Siamese network allow for batches of training data reduction without diminishing the performance of fault detection. Finally, we utilize monitoring statistics to complete the fault detection process. Two batch process cases involving the penicillin fermentation process and the semiconductor etching process demonstrate the superior fault detection performance of the proposed SDeNPE over the other comparison methods.

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An adaptive strategy for time‐varying batch process fault prediction based on stochastic configuration network

Liu,

Zhao,

Hui

et al. 2024

Journal of Chemometrics

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Fault prediction ensures safe and stable production, and cuts maintenance costs. Due to the changing operating conditions that lead to the changes in the characteristics of industrial processes, there is a need to monitor the fault state of batch processes in real‐time and to accurately predict fault trends. An adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction is proposed. Firstly, SFA is used to extract the time‐varying features of process data and establish the update index of the NPE model. Then, to extract local nearest‐neighbor features and reconstruct them by the NPE model with adaptive update capability, square prediction error (SPE) statistics are constructed as fault state features based on the reconstructed error. Further, the hunter‐prey optimization (HPO) algorithm optimizes the weights and biases in the stochastic configuration network, and the singular value decomposition (SVD) and QR decomposition of column rotation are introduced to solve the ill‐posed problem of SCN and obtain the prediction model of ISCN. Finally, the obtained statistics SPE is formed into a time series, and the ISCN model is used to predict the process state trend. The effectiveness of the proposed algorithm is verified by case studies of industrial‐scale penicillin fermentation processes and the Hot strip mill process.

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Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm

Cited by 5 publications

References 39 publications

An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling

An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling

SiameseDeNPEnetwork framework for fault detection of batch process

An adaptive strategy for time‐varying batch process fault prediction based on stochastic configuration network

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