A fault detection method based on sparse dynamic canonical correlation analysis

Hu, Xuguang; Wu, Ping; Pan, Haipeng; He, Yuchen

doi:10.1002/cjce.25124

Cited by 2 publications

(1 citation statement)

References 47 publications

(70 reference statements)

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“…The history and trends of PLS/CCA techniques, especially PLS, follow the same lines as the development of PCA. Works have been developed for applications concerning batch processes [168][169][170][171][172][173][174], dynamics [169,[175][176][177][178][179][180][181][182][183][184][185][186][187][188][189][190][191][192], nonlinearity [119,185,[193][194][195][196][197][198], multimodality [199][200][201][202], multiple time scales [119,[203][204][205], multiple units and/or stages [125,170,193,[206][207][208]…”

Section: Pls/cca Assumptionmentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

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Section: Pls/cca Assumptionmentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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Nonlinear dynamic process monitoring based on latent mapping embedding deep neural networks

Yu,

Wang,

Wang

et al. 2024

Can J Chem Eng

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In industrial processes, complex nonlinearity and dynamics generally exist, making it challenging to achieve good results using conventional process monitoring methods. In this paper, a latent mapping embedding neural network method (LMNN) is proposed for efficient monitoring of nonlinear dynamic processes. First, a deep neural network (DNN) is employed to acquire features of state variables from nonlinear process data and expand them along with the input to a new feature subspace. Second, a latent mapping (LM) method is used to map the high‐dimensional feature subspace to a low‐dimensional subspace that includes the most beneficial time series information. Then the entire neural network and regression parameters are obtained through an end‐to‐end learning manner, through which the nonlinearity and process dynamics are well characterized. Subsequently, prediction error‐based residual is generated and the monitoring model is established. The performance of the proposed method is verified through a simulation of penicillin production process and an actual fermentation process of penicillin. Comparisons with state‐of‐the‐art methods are carried out, and results validate the effectiveness and superiority of the proposed method.

show abstract

A fault detection method based on sparse dynamic canonical correlation analysis

Cited by 2 publications

References 47 publications

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Nonlinear dynamic process monitoring based on latent mapping embedding deep neural networks

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