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Mathematics
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Mathematical Methods for Fault Diagnosis and Fault-Tolerant Control Systems
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Mathematical Methods for Fault Diagnosis and Fault-Tolerant Control Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3622

Special Issue Editors

Prof. Dr. Junqi Xu

E-Mail Website
Guest Editor
National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
Interests: levitation control technology of maglev train and coupling vibration between maglev train and track
Dr. Fei Ni

E-Mail Website
Guest Editor
National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
Interests: robust control & reliability analysis of maglev train; analysis and optimization of integrated power electric-traffic network
Dr. Yougang Sun

E-Mail Website
Guest Editor
Institute of Rail Transit, Tongji University, Shanghai 201804, China
Interests: maglev trains; offshore cranes; quay cranes and nonlinear control with applications to mechatronic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fault diagnosis and fault-tolerant control systems (FDFTCS) are widely used to enhance the reliability and fault-tolerant capability of practical systems, including power systems, transportation systems, chemical industry systems and other safety-critical systems. Over the last decade, complex mathematical methods have made great contributions to the development of these systems and solid theoretical foundations and fruitful results have been reported in relation to FDFTCS technologies. However, the modern systems are becoming more and more complicated, and the faults can be seen in their actuators, sensors, process hardware and software components, and even human operators’ decisions. The fault diagnosis and fault-tolerant control of modern systems faces significant problems, necessitating the development of novel theories and mathematical methods for system analysis, diagnosis and control.

We invite prospective authors to submit their contributions for fruitful interdisciplinary cooperation and the exchange of new ideas and experiences, as well as to identify new issues and challenges and to shape future directions and trends for research in fault diagnosis and fault-tolerant control technology.

Potential topics include, but are not limited to:

  • Mathematics-driven static fault diagnosis approaches
  • Observer-based fault diagnosis framework: model-based designs
  • Knowledge-based fault diagnosis
  • Passive and active fault-tolerant control method
  • Mathematical models in prognostic and health management
  • Sensors and soft sensors for supervision and control
  • Mathematics-driven intelligent methods for fault-tolerant control

Prof. Dr. Junqi Xu
Dr. Fei Ni 
Dr. Yougang Sun
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • mathematical model in FDFTCS
  • fault detection and isolation
  • fault estimation and diagnosis
  • mathematics-driven fault-tolerant control
  • mathematics-driven sensors and soft sensors
  • mathematical methods

Published Papers (3 papers)

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Research

19 pages, 6240 KiB  
Article
Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors
by Su-Dan Huang, Zhixiang Lin, Guang-Zhong Cao, Ningpeng Liu, Hongda Mou and Junqi Xu
Mathematics 2023, 11(19), 4067; https://doi.org/10.3390/math11194067 - 25 Sep 2023
Viewed by 955
Abstract
Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position [...] Read more.
Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters. Full article
(This article belongs to the Special Issue Mathematical Methods for Fault Diagnosis and Fault-Tolerant Control Systems)
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20 pages, 8231 KiB  
Article
Real-Time Malfunction Detection of Maglev Suspension Controllers
by Su-Mei Wang, You-Wu Wang, Yi-Qing Ni and Yang Lu
Mathematics 2023, 11(19), 4045; https://doi.org/10.3390/math11194045 - 24 Sep 2023
Viewed by 1145
Abstract
This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data [...] Read more.
This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data acquisition unit. The software module of the system consists of codes for three functions: (i) the identification of time intervals in relation to the passage of each suspension controller via synchrosqueezing transform; (ii) the extraction of a feature index (FI) sequence synthesized by modulating the response amplitude, frequency, and running speed; and (iii) the formulation of a Bayesian dynamic linear model for real-time malfunction detection in maglev suspension controllers. For verification of the proposed monitoring system and malfunction detection algorithm, full-scale tests have been conducted on an maglev test line using the devised system, where a maglev train was run at different speeds with malfunction occurring in the suspension controllers. The malfunction detection results of the proposed approach are exemplified via comparison with the recorded suspension gaps after the trial run of the maglev train. The fidelity of the results obtained using the extracted FI sequence and using the raw monitoring data are compared. The superiority of the proposed malfunction detection algorithm is also discussed via comparison with the results of the different train speeds. Full article
(This article belongs to the Special Issue Mathematical Methods for Fault Diagnosis and Fault-Tolerant Control Systems)
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18 pages, 3215 KiB  
Article
SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection
by Jianmin Zhou, Lulu Liu and Xiwen Shen
Mathematics 2023, 11(17), 3677; https://doi.org/10.3390/math11173677 - 25 Aug 2023
Cited by 1 | Viewed by 698
Abstract
In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the [...] Read more.
In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the number of depth layers used optimization algorithms that tend to increase computation time. In addition, the data under single feature selection are not sufficiently representative, and effective features are easily lost. To solve these problems, this article proposes an infrared fault diagnosis model for rolling bearings based on integration of principal component analysis and singular value decomposition (IPS) and the stacked BLS with self-selected depth model (SSDStacked-BLS). First, 72 second-order statistical features are extracted from the pre-processed infrared images of rolling bearings. Next, feature selection is performed using IPS. he IPS feature selection module consists of principal component analysis (PCA) and singular value decomposition (SVD). The feature selection is performed by PCA and SVD separately, which are then stitched together to form a new feature. This ensures a comprehensive coverage of infrared image features. Finally, the acquired features are input into SSDStacked-BLS. This model establishes a data storage group for the residual training characteristics of stacked BLS, adding one block at a time. The accuracy rate of each newly added block is output and saved to the data storage group. If the diagnostic rate fails to increase three consecutive times, the block stacking is stopped and the results are output. IPS-SSDStacked-BLS achieved an accuracy of 0.9667 in 0.1775 s. This is almost five times faster than stacked BLS optimized using the grid search method. Compared with the original BLS, its accuracy was 0.0445 higher and the time was approximated. Compared with IPS-SVM, IPS-RF, IPS-1DCNN and 2DCNN, IPS-SSDStacked-BLS was more advantageous in terms of accuracy and time consumption. Full article
(This article belongs to the Special Issue Mathematical Methods for Fault Diagnosis and Fault-Tolerant Control Systems)
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