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Processes
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Fault Diagnosis, Fault Tolerant Control and Process Simulation of Nonlinear...
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Fault Diagnosis, Fault Tolerant Control and Process Simulation of Nonlinear Systems

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1729

Special Issue Editors

Prof. Dr. Gloria Lilia Osorio-Gordillo

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca 62490, Morelos, Mexico
Interests: nonlinear systems; observer design, fault diagnosis; fault-tolerant control; multi-model representations; Takagi–Sugeno; LPV systems; singular systems
Dr. Marouane Alma

E-Mail Website
Guest Editor
CRAN-CNRS (UMR 7039), Universitè de Lorraine, IUT Longwy, 186, Rue de Lorraine, 54400 Cosnes et Romain, France
Interests: nonlinear systems; nonlinear observer; observer and parameter estimation; stability analysis; adaptive control; robust control; fault diagnosis

Special Issue Information

Dear Colleagues,

For several years, studying process faults has been of great interest. However, there are still many approaches to be explored in nonlinear systems.

Fault diagnosis refers to the procedure of determining whether a fault occurs in a system, including identifying when, where, what kind of a fault, and what has been the impact of the fault. Fault diagnosis provides useful information for fault-tolerant control schemes, allowing the exploration of tolerance to different magnitudes and types of faults from developing controllers.

This Special Issue on “Fault Diagnosis, Fault Tolerant Control and Process Simulation of Nonlinear Systems” covers recent advances in developing different approaches to deal with process faults. Topics include, but are not limited to:

  • Nonlinear processes;
  • Fault detection and isolation systems;
  • Fault diagnosis systems;
  • Observer design for fault systems;
  • Adaptive fault-tolerant control.

Prof. Dr. Gloria Lilia Osorio-Gordillo
Dr. Marouane Alma
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

  • modeling
  • simulation
  • health processes
  • fault diagnosis
  • fatult tolerant control
  • stability analysis

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Published Papers (3 papers)

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Research

12 pages, 3410 KiB  
Article
Experimental Study on Biodiesel Production in a Continuous Tubular Reactor with a Static Mixer
by Abisai Acevedo-Quiroz, Edgardo de Jesús Carrera-Avendaño, Noemi Acevedo-Quiroz, Peggy Elizabeth Alvarez-Gutiérrez, Monica Borunda and Manuel Adam-Medina
Processes 2024, 12(12), 2859; https://doi.org/10.3390/pr12122859 - 13 Dec 2024
Viewed by 429
Abstract
This research on biodiesel production aims to improve energy processes to advance towards a sustainable economy. This study focuses on improving the biodiesel conversion efficiency in a helical tubular reactor coupled with a static mixer. A 23 factorial design was used to [...] Read more.
This research on biodiesel production aims to improve energy processes to advance towards a sustainable economy. This study focuses on improving the biodiesel conversion efficiency in a helical tubular reactor coupled with a static mixer. A 23 factorial design was used to evaluate how variables such as the molar ratio of alcohol–oil (4:1–8:1), residence time (4–8 min), and catalyst concentration (0.5–1 wt%) affect the transesterification process. Soybean oil and methanol were used, with NaOH as a catalyst at 60 °C. The results show that the residence time and catalyst concentration are key factors in increasing biodiesel production by up to 10%. An experimental yield of 84.97% was obtained with a molar ratio of 6:1 alcohol–oil, 0.9 wt% NaOH, and a reaction time of 6 min. The experimental design predicted a yield of 91% with a molar ratio of 4:1 alcohol–oil, 1 wt% NaOH, and a reaction time of 8 min, with a deviation of 1.88% from the experimental values. The fit of the experimental model was R2 = 0.9632. These findings are valuable for improving the transesterification process and the development of biodiesel in continuous flow reactors. Full article
(This article belongs to the Special Issue Fault Diagnosis, Fault Tolerant Control and Process Simulation of Nonlinear Systems)
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18 pages, 10262 KiB  
Article
Fault Diagnosis of Mechanical Rolling Bearings Using a Convolutional Neural Network–Gated Recurrent Unit Method with Envelope Analysis and Adaptive Mean Filtering
by Huiyi Zhu, Zhen Sui, Jianliang Xu and Yeshen Lan
Processes 2024, 12(12), 2845; https://doi.org/10.3390/pr12122845 - 12 Dec 2024
Viewed by 383
Abstract
Rolling bearings are vital components in rotating machinery, and their reliable operation is crucial for maintaining the stability and efficiency of mechanical systems. However, fault detection in rolling bearings is often hindered by noise interference in complex industrial environments. To overcome this challenge, [...] Read more.
Rolling bearings are vital components in rotating machinery, and their reliable operation is crucial for maintaining the stability and efficiency of mechanical systems. However, fault detection in rolling bearings is often hindered by noise interference in complex industrial environments. To overcome this challenge, this paper presents a novel fault diagnosis method for rolling bearings, combining Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), integrated with the envelope analysis and adaptive mean filtering techniques. Initially, envelope analysis and adaptive mean filtering are applied to suppress random noise in the bearing signals, thereby enhancing the visibility of fault features. Subsequently, a deep learning model that combines a CNN and a GRU is developed: the CNN extracts spatial features, while the GRU captures the temporal dependencies between these features. The integration of the CNN and GRU significantly improves the accuracy and robustness of fault diagnosis. The proposed method is validated using the CWRU dataset, with the experimental results achieving an average accuracy of 99.25%. Additionally, the method is compared to four classical fault diagnosis models, demonstrating superior performance in terms of both diagnostic accuracy and generalization ability. The results, supported by various visualization techniques, show that the proposed approach effectively addresses the challenges of fault detection in rolling bearings under complex industrial conditions. Full article
(This article belongs to the Special Issue Fault Diagnosis, Fault Tolerant Control and Process Simulation of Nonlinear Systems)
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15 pages, 6082 KiB  
Article
Time/Frequency Feature-Driven Ensemble Learning for Fault Detection
by Yunchu Miao, Zhen Li and Maoyin Chen
Processes 2024, 12(10), 2099; https://doi.org/10.3390/pr12102099 - 27 Sep 2024
Viewed by 606
Abstract
This study addresses the problem of fault detection in industrial processes by developing a time/frequency feature-driven ensemble learning method. In contrast to the current works based on time domain ensemble learning, this approach adequately integrates the critical frequency domain information. The frequency domain [...] Read more.
This study addresses the problem of fault detection in industrial processes by developing a time/frequency feature-driven ensemble learning method. In contrast to the current works based on time domain ensemble learning, this approach adequately integrates the critical frequency domain information. The frequency domain information can be used to effectively enhance the fault detection performance in ensemble learning. Here, the feature ensemble net (FENet) is chosen to capture the time domain feature. The power spectral density (PSD)-based frequency domain feature extraction network can capture the frequency domain features. Bayesian inference can then be used to combine the fault detection results that rely on time/frequency domain features. The simulations of the Tennessee Eastman Process (TEP) demonstrate that the proposed method significantly outperforms traditional methods. The average fault detection rate (FDR) of TEP faults 3, 5, 9, 15, 16, and 21 is 90.63%, much higher than that of 75% by FENet with one feature transformation layer, and those of about 4% by principal component analysis (PCA) and dynamic PCA (DPCA). This research provides a promising framework for more advanced and reliable fault detection in industrial applications. Full article
(This article belongs to the Special Issue Fault Diagnosis, Fault Tolerant Control and Process Simulation of Nonlinear Systems)
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