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 427

Special Issue Editors


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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

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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

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Keywords

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

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Published Papers (1 paper)

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Research

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 319
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
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