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Processes
Special Issues
Condition Monitoring and the Safety of Industrial Processes
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Condition Monitoring and the Safety of Industrial Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3106

Special Issue Editors

Dr. Jie Zhang

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: advanced process control; process fault detection and diagnosis; neural networks and neuro-fuzzy systems; multivariate statistical process control; optimal control of batch processes
Special Issues, Collections and Topics in MDPI journals
Dr. Zhe Zhou

E-Mail Website
Guest Editor
School of Engineering, Huzhou University, Huzhou 313000, China
Interests: fault diagnosis; machine learning; process monitoring; big data
Dr. Dong Gao

E-Mail Website
Guest Editor
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Interests: process simulaiton; safety analysis; deep learning

Special Issue Information

Dear Colleagues,

With the rapid developments in process control and automation, the Internet of Things, process measurement and instrumentation, and process intensification, industrial processes are becoming more automated and integrated. The safety of such highly automated and integrated industrial processes is becoming increasingly important. In recent decades, a broad range of techniques for the safety of industrial processes have been developed. Examples of these include model-based approaches, knowledge-based approaches, and data-driven approaches based on multivariate statistical data analysis and machine learning techniques. The rapid development of AI techniques in recent years has resulted in novel tools for addressing the safety of industrial processes.

This Special Issue aims to bring together the recent advances in innovative techniques for improving the safety of industrial processes. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Fault detection;
  • Fault diagnosis;
  • Fault prognosis;
  • Process monitoring;
  • Multivariate statistical process control;
  • Machine learning for process safety;
  • Fault tolerant control.

Dr. Jie Zhang
Dr. Zhe Zhou
Dr. Dong Gao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process monitoring
  • fault detection
  • fault diagnosis
  • fault prognosis
  • safety
  • multivariate statistical process monitoring
  • principal component analysis
  • predictive maintenance

Published Papers (2 papers)

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Research

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24 pages, 2620 KiB  
Article
A Novel Convolutional LSTM Network Based on the Enhanced Feature Extraction for the Transmission Line Fault Diagnosis
by Youfu Lu, Xuehan Zheng, He Gao, Xiaoying Ding and Xuefei Liu
Processes 2023, 11(10), 2955; https://doi.org/10.3390/pr11102955 - 12 Oct 2023
Viewed by 876
Abstract
Recently, the traditional transmission line fault diagnosis approaches cannot handle the variables’ dynamic coupling properties, and they also ignore the local structure feature information during the feature extraction. To figure out these issues, a novel enhanced feature extraction based convolutional LSTM (ECLSTM) approach [...] Read more.
Recently, the traditional transmission line fault diagnosis approaches cannot handle the variables’ dynamic coupling properties, and they also ignore the local structure feature information during the feature extraction. To figure out these issues, a novel enhanced feature extraction based convolutional LSTM (ECLSTM) approach is developed to diagnose the transmission line fault in this paper. Our work has three main contributions: (1) To tackle the dynamic coupling characteristics of the process variables, the statistics analysis (SA) method is first employed to calculate different statistical features of the transmission line’s original data, where the original datasets are transformed into the subsequently used statistics datasets; (2) The statistics comprehensive feature preserving (SCFP) is then proposed to maintain both the global and local structure features of the constructed statistics datasets, where the locality structure preserving technique is incorporated into the principal component analysis (PCA) model to extract the features from the statistics datasets; (3) To effectively diagnose the transmission line’s fault, the SCFP based convolutional LSTM fault diagnosis scheme is constructed to classify the global and local statistical structure features of fault snapshot dataset, because of its ability to exploit the temporal dependencies and spatial correlations of the extracted statistical features. Detailed experiments and comparisons on the datasets of the simulated power system are performed to prove the excellent performance of the ECLSTM based fault diagnosis scheme. Full article
(This article belongs to the Special Issue Condition Monitoring and the Safety of Industrial Processes)
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Review

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34 pages, 4296 KiB  
Review
Comprehensive Review of Safety Studies in Process Industrial Systems: Concepts, Progress, and Main Research Topics
by Jialu Zhang, Haojie Ren, Hao Ren, Yi Chai, Zhaodong Liu and Xiaojun Liang
Processes 2023, 11(8), 2454; https://doi.org/10.3390/pr11082454 - 15 Aug 2023
Viewed by 1874
Abstract
This paper focuses on reviewing past progress in the advancement of definitions, methods, and models for safety analysis and assessment of process industrial systems and highlighting the main research topics. Based on the analysis of the knowledge with respect to process safety, the [...] Read more.
This paper focuses on reviewing past progress in the advancement of definitions, methods, and models for safety analysis and assessment of process industrial systems and highlighting the main research topics. Based on the analysis of the knowledge with respect to process safety, the review covers the fact that the entire system does not have the ability to produce casualties, health deterioration, and other accidents, which ultimately cause human life threats and health damage. And, according to the comparison between safety and reliability, when a system is in an unreliable state, it must be in an unsafe state. Related works show that the main organizations and regulations are developed and grouped together, and these are also outlined in the literature. The progress and current research topics of the methods and models have been summarized and discussed in the analysis and assessment of safety for process industrial systems, which mainly illustrate that the dynamic operational safety assessment under the big data challenges will become the research direction, which will change the future study situation. Full article
(This article belongs to the Special Issue Condition Monitoring and the Safety of Industrial Processes)
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