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Machines
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Performance-Explainable Fault Diagnosis and Advanced Control Techniques for Industrial Dynamic...
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Performance-Explainable Fault Diagnosis and Advanced Control Techniques for Industrial Dynamic Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2882

Special Issue Editors

Dr. Guangtao Ran

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: networked systems; fault diagnosis; event-based control; nonlinear control
Special Issues, Collections and Topics in MDPI journals
Dr. Hongtian Chen

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Interests: fault detection and diagnosis; high-speed trains; data mining and analytics; machine learning; quantum computation
Special Issues, Collections and Topics in MDPI journals
Dr. Zhuofu Pan

E-Mail Website
Guest Editor
School of Microelectronics and Physics, Hunan University of Technology and Business, Changsha, China
Interests: explainable artificial intelligence; fault diagnosis; data incomplete and imbalance problem; data-driven system observation and identification

Special Issue Information

Dear Colleagues,

Fault diagnosis and control techniques play a significant role in ensuring the smooth and safe operation of industrial systems. While achieving a higher test performance is important, the industry places greater emphasis on the security and interpretability of established models. Despite achieving high classification or prediction performance, real-world industries are hesitant to adopt models that lack transparency or performance guarantees, as even a small oversight can lead to unpredictable damage to life and property. Therefore, the development of performance-explainable fault diagnosis and control techniques is crucial to establish trust in model decisions and ensure their safe applications. Techniques that enhance model understanding fall within this scope, including model-based error/state convergence proofs, as well as data-based visualization and interpretability analysis.
This Special Issue addresses the need to develop performance-explainable advanced technologies, considering fault diagnosis and control applications in any industrial systems. The scope of this Special Issue includes, but is not limited to, the following topics

(1)    Fault detection, isolation, and estimation of industrial dynamic systems;
(2)    Visualization and interpretability analysis toward extracted features or model behavior;
(3)    Stability analysis related to state observers, diagnosticians, and controllers;
(4)    Predictive- and learning-based control to improve the security and stability of industrial systems;
(5)    Fault detection of smart grid, aerospace system, and UAVs;
(6)    Fuzzy control and sliding mode control of nonlinear systems;
(7)    Neural-network-assisted system dynamics analysis and parameter identification;
(8)    Applications of advanced control in industrial systems.

We welcome original research articles, review articles, and case studies that demonstrate novel approaches and significant contributions to the field of performance-explainable fault diagnosis and advanced control techniques for industrial dynamic systems.

Dr. Guangtao Ran
Dr. Hongtian Chen
Dr. Zhuofu Pan
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. Machines 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

  • fault diagnosis
  • advanced control technologies
  • explainable performance analysis
  • advanced industrial modeling applications

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Published Papers (2 papers)

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Research

20 pages, 7621 KiB  
Article
Enhancing Photovoltaic-Powered DC Shunt Motor Performance for Water Pumping through Fuzzy Logic Optimization
by Abdulaziz Alkuhayli, Abdullah M. Noman, Abdullrahman A. Al-Shamma’a, Akram M. Abdurraqeeb, Mohammed Alharbi, Hassan M. Hussein Farh and Affaq Qamar
Machines 2024, 12(7), 442; https://doi.org/10.3390/machines12070442 - 27 Jun 2024
Cited by 1 | Viewed by 880
Abstract
This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and [...] Read more.
This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios. Full article
(This article belongs to the Special Issue Performance-Explainable Fault Diagnosis and Advanced Control Techniques for Industrial Dynamic Systems)
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25 pages, 8414 KiB  
Article
A Fault Prediction Method for CNC Machine Tools Based on SE-ResNet-Transformer
by Zhidong Wu, Liansheng He, Wei Wang, Yongzhi Ju and Qiang Guo
Machines 2024, 12(6), 418; https://doi.org/10.3390/machines12060418 - 18 Jun 2024
Viewed by 1140
Abstract
Aiming at the problem that predicted data do not reflect the operating status of computer numerical control (CNC) machine tools, this article proposes a new combined model based on SE-ResNet and Transformer for CNC machine tool failure prediction. Firstly, the Transformer model is [...] Read more.
Aiming at the problem that predicted data do not reflect the operating status of computer numerical control (CNC) machine tools, this article proposes a new combined model based on SE-ResNet and Transformer for CNC machine tool failure prediction. Firstly, the Transformer model is utilised to build a non-linear temporal feature mapping using the attention mechanism in multidimensional data. Secondly, the predicted data are transformed into 2D features by the SE-ResNet model, which is adept at processing 2D data, and the spatial feature relationships between predicted data are captured, thus enhancing the state recognition capability. Through experiments, data involving the CNC machine tools in different states are collected to build a dataset, and the method is validated. The SE-ResNet-Transformer model can accurately predict the state of CNC machine tools with a recognition rate of 98.56%. Results prove the effectiveness of the proposed method in CNC machine tool failure prediction. The SE-ResNet-Transformer model is a promising approach for CNC machine tool failure prediction. The method shows great potential in improving the accuracy and efficiency of CNC machine tool failure prediction. Feasible methods are provided for precise control of the state of CNC machine tools. Full article
(This article belongs to the Special Issue Performance-Explainable Fault Diagnosis and Advanced Control Techniques for Industrial Dynamic Systems)
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