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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: 28 February 2025 | Viewed by 8081

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

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Published Papers (5 papers)

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Research

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29 pages, 18532 KiB  
Article
B-TBM: A Novel Deep Learning Model with Enhanced Loss Function for HAZOP Risk Classification Using Natural Language Statistical Laws
by Binxin Xu, Duhui Lu, Dong Gao and Beike Zhang
Processes 2024, 12(11), 2373; https://doi.org/10.3390/pr12112373 - 29 Oct 2024
Viewed by 593
Abstract
HAZOP is a paradigm of industrial safety, and the introduction of deep learning-based HAZOP text categorization marks the arrival of an intelligent era of safety analysis. However, existing risk analysis methods have limitations in processing complex texts and extracting deep risk features. To [...] Read more.
HAZOP is a paradigm of industrial safety, and the introduction of deep learning-based HAZOP text categorization marks the arrival of an intelligent era of safety analysis. However, existing risk analysis methods have limitations in processing complex texts and extracting deep risk features. To solve this problem, this paper proposes a novel HAZOP risk event classification model based on BERT, BiLSTM, and TextCNN. The complexity of HAZOP text is revealed by introducing statistical laws of natural language, such as Zipf’s law and Heaps’ law, and the outputs of different levels of BERT are further combined linearly to collaborate with BiLSTM and TextCNN to capture long-term dependency and local contextual information for a more accurate classification task. Meanwhile, an improved loss function is proposed to effectively solve the deficiencies of the traditional cross-entropy loss function in the mislabeling process and improve the generalization ability of the model. It is experimentally demonstrated that the accuracy of the model is improved by 3% to 4% compared to the traditional BERT model in the task of severity and possibility classification of HAZOP reports. This study not only improves the accuracy and efficiency of HAZOP risk analysis, but also provides new ideas and methods for the application of natural language processing in industrial safety. Full article
(This article belongs to the Special Issue Condition Monitoring and the Safety of Industrial Processes)
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20 pages, 8952 KiB  
Article
Research on High-Frequency Torsional Oscillation Identification Using TSWOA-SVM Based on Downhole Parameters
by Tao Zhang, Wenjie Zhang, Zhuoran Meng, Jun Li and Miaorui Wang
Processes 2024, 12(10), 2153; https://doi.org/10.3390/pr12102153 - 2 Oct 2024
Viewed by 844
Abstract
The occurrence of downhole high-frequency torsional oscillations (HFTO) can lead to the significant damage of drilling tools and can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale algorithm optimization support vector machine [...] Read more.
The occurrence of downhole high-frequency torsional oscillations (HFTO) can lead to the significant damage of drilling tools and can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale algorithm optimization support vector machine (TSWOA-SVM) for accurate HFTO identification. Initially, the population is initialized using Fuch chaotic mapping and a reverse learning strategy to enhance population quality and accelerate the whale optimization algorithm (WOA) convergence. Subsequently, the hyperbolic tangent function is introduced to dynamically adjust the inertia weight coefficient, balancing the global search and local exploration capabilities of WOA. A simulated annealing strategy is incorporated to guide the population in accepting suboptimal solutions with a certain probability, based on the Metropolis criterion and temperature, ensuring the algorithm can escape local optima. Finally, the optimized whale optimization algorithm is applied to enhance the support vector machine, leading to the establishment of the HFTO identification model. Experimental results demonstrate that the TSWOA-SVM model significantly outperforms the genetic algorithm-SVM (GA-SVM), gray wolf algorithm-SVM (GWO-SVM), and whale optimization algorithm-SVM (WOA-SVM) models in HFTO identification, achieving a classification accuracy exceeding 97%. And the 5-fold crossover experiment showed that the TSWOA-SVM model had the highest average accuracy and the smallest accuracy variance. Overall, the non-parametric TSWOA-SVM algorithm effectively mitigates uncertainties introduced by modeling errors and enhances the accuracy and speed of HFTO identification. By integrating advanced optimization techniques, this method minimizes the influence of initial parameter values and balances global exploration with local exploitation. The findings of this study can serve as a practical guide for managing near-bit states and optimizing drilling parameters. Full article
(This article belongs to the Special Issue Condition Monitoring and the Safety of Industrial Processes)
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22 pages, 4046 KiB  
Article
Model-Free Adaptive Sliding Mode Control Scheme Based on DESO and Its Automation Application
by Xiaohua Wei, Zhen Sui, Hanzhou Peng, Feng Xu, Jianliang Xu and Yulong Wang
Processes 2024, 12(9), 1950; https://doi.org/10.3390/pr12091950 - 11 Sep 2024
Viewed by 639
Abstract
This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept [...] Read more.
This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application. Full article
(This article belongs to the Special Issue Condition Monitoring and the Safety of Industrial Processes)
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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 1184
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

Jump to: Research

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
Cited by 2 | Viewed by 4192
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Research on high frequency torsional oscillation identification using TSWOA-SVM based on downhole parameters
Authors: Tao Zhang; Wenjie Zhang; Zhuoran Meng; Jun Li; Miaorui Wang
Affiliation: Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing University of Information Science and Technology, Beijing 100101, China
Abstract: The occurrence of high-frequency torsional oscillation (HFTO) can damage drilling tools and reduce efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale optimization algorithm based on support vector machines (TSWOA-SVM) to identify HFTO accurately. First, the population is initialized using the Fuch chaotic mapping and reverse learning strategy to improve population quality and accelerate the WOA convergence. Then, the hyperbolic tangent function is introduced to dynamically adjust the inertia weight coefficient to balance the global search and local exploration capabilities of WOA. Next, a simulated annealing strategy is incorporated, guiding the population to accept suboptimal solutions with a certain probability based on the Metropolis criterion and temperature, ensuring the algorithm can escape local optima. Finally, the improved whale optimization algorithm is used to optimize the support vector machine, and the HFTO identification model is established. Experimental results show that the TSWOA-SVM algorithm model is significantly superior to the genetic algorithm-SVM (GA-SVM), gray wolf algorithm-SVM (GWO-SVM), and whale optimization algorithm-SVM (WOA-SVM) models in identifying HFTO, achieving over 97% classification accuracy. The results of this study can be used as a practical guide for the management and planning of near-bit states and optimizing drilling parameters.

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