Enhancing fault detection and diagnosis systems for a chemical process: a study on convolutional neural networks and transfer learning

e Souza, Ana Cláudia Oliveira; de Souza, Maurício Bezerra; da Silva, Flávio Vasconcelos

doi:10.1007/s12530-023-09523-y

Cited by 4 publications

(1 citation statement)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are works involving applications of process monitoring using several network architectures, such as recurrent networks [351][352][353][354][355][356][357][358][359][360][361][362][363][364][365][366][367], convolutional networks [299,349,[368][369][370][371][372][373][374][375][376][377][378][379][380][381][382], autoencoders [282,365,379,, generative adversarial networks [406][407][408][409][410][411][412][413][414][415][416], Transformers [417]…”

Section: Applications In Process Monitoringmentioning

confidence: 99%

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

Melo,

Câmara,

Pinto

2024

Processes

View full text Add to dashboard Cite

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.

show abstract

Section: Applications In Process Monitoringmentioning

confidence: 99%

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

Melo,

Câmara,

Pinto

2024

Processes

View full text Add to dashboard Cite

show abstract

Adaptive fault diagnosis for high-purity carbonate process based on unsupervised and transfer learning

Shi,

Ge,

Liu

2024

Chemical Engineering Science

View full text Add to dashboard Cite

A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning

Chen,

Zhang,

Zhou

et al. 2024

Entropy

View full text Add to dashboard Cite

A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset.

show abstract

Enhancing fault detection and diagnosis systems for a chemical process: a study on convolutional neural networks and transfer learning

Cited by 4 publications

References 73 publications

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

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

Adaptive fault diagnosis for high-purity carbonate process based on unsupervised and transfer learning

A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning

Contact Info

Product

Resources

About