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Processes
Special Issues
Fault Diagnosis Process and Evaluation in Systems Engineering
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Fault Diagnosis Process and Evaluation in Systems Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 5379

Special Issue Editors

Dr. Shaoke Wan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: intelligent equipment; online monitoring; testing equipment; bearing rotor system; intelligent diagnosis and evaluation
Dr. Naipeng Li

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: fault diagnosis of machinery; degradation modeling; remaining useful life prediction; intelligent maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Zijian Qiao

E-Mail Website
Guest Editor
School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, China
Interests: mechanical fault diagnosis; weak signal detection; nonlinear dynamics; AI-enabled fault diagnosis and intelligent maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fault diagnosis plays a crucial role in ensuring system reliability, safety, and performance. With the increasing popularity of complex systems, fault diagnosis and evaluation have become important research topics in the field of systems engineering.

This Special Issue will cover a variety of systems and applications, including power, machinery, aerospace, chemical, and medical equipment. The articles will provide detailed introductions to fault diagnosis methods and technologies, including model-based methods, data-driven methods, and hybrid methods. Additionally, the articles will discuss the design and evaluation of fault diagnosis systems, including fault detection, isolation, and prediction, as well as system reliability and availability analysis.

Concurrently, this Special Issue will explore fault management and prevention strategies, including maintenance and repair strategies, and designing systems to improve reliability and durability. These discussions will help readers understand the importance and applications of fault diagnosis in systems engineering.

Overall, this Special Issue aims to provide readers with an in-depth understanding and practical guide to the fault diagnosis process and evaluation, aiming to help readers understand, design, and apply fault diagnosis systems to improve system reliability, safety, and performance.

This Special Issue aims to provide a platform for researchers and practitioners to share their recent advances, innovations, and challenges in fault diagnosis and evaluation. We welcome high-quality original research papers, review articles, and case studies that address the following topics but not limited to:

  • Novel techniques for fault diagnosis and evaluation;
  • Design and evaluation of fault diagnosis systems;
  • Fault management and prevention strategies;
  • Feature extraction and selection for fault detection and evaluation;
  • Fault diagnosis and evaluation based on vibration analysis, acoustic emission, oil analysis, or the fusion of multi-source signals;
  • Machine learning and artificial intelligence in fault diagnosis and evaluation;
  • Case studies of real-world fault diagnosis and evaluation in engineering systems

Dr. Shaoke Wan
Dr. Naipeng Li
Dr. Zijian Qiao
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

  • fault diagnosis
  • fault prediction
  • system engineering
  • feature extraction
  • system security and reliability
  • multi-source information fusion
  • reliability analysis

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

23 pages, 9398 KiB  
Article
Analysis of the Effect of Structural Parameters on the Internal Flow Field of Composite Curved Inlet Body Hydrocyclone
by Yanchao Wang, Hu Han, Zhitao Liang, Huanbo Yang, Feng Li, Wen Zhang and Yanrui Zhao
Processes 2024, 12(12), 2654; https://doi.org/10.3390/pr12122654 - 25 Nov 2024
Viewed by 266
Abstract
To enhance the classification efficiency of hydrocyclones, this study introduces a novel hydrocyclone design featuring a composite curved-inlet-body structure. Through numerical simulations, the internal flow field characteristics of this structure are thoroughly investigated. The results reveal several key findings: when the diameter of [...] Read more.
To enhance the classification efficiency of hydrocyclones, this study introduces a novel hydrocyclone design featuring a composite curved-inlet-body structure. Through numerical simulations, the internal flow field characteristics of this structure are thoroughly investigated. The results reveal several key findings: when the diameter of the overflow tube is reduced below a critical threshold, the axial velocity exhibits predominantly downward movement within the outer cyclone, accompanied by substantial recirculation, leading to a loss of effective separation. Moreover, both static pressure and tangential velocity are largely independent of the insertion depth of the overflow tube. In contrast, the diameter of the bottom flow opening plays a crucial role in determining flow dynamics within the hydrocyclone. An excessively large or small bottom opening leads to flow instabilities, causing fluctuations that disrupt the uniformity of the flow field. Additionally, a small height-to-diameter ratio exacerbates flow instability, increasing turbulence intensity and resulting in irregular fluctuations in the LZVV. These findings provide important theoretical insights for the design of more efficient hydrocyclone separation structures. Full article
(This article belongs to the Special Issue Fault Diagnosis Process and Evaluation in Systems Engineering)
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20 pages, 10237 KiB  
Article
Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin
by Amged Sayed, Samah Alshathri and Ezz El-Din Hemdan
Processes 2024, 12(11), 2357; https://doi.org/10.3390/pr12112357 - 27 Oct 2024
Viewed by 651
Abstract
In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use [...] Read more.
In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions. Full article
(This article belongs to the Special Issue Fault Diagnosis Process and Evaluation in Systems Engineering)
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21 pages, 5915 KiB  
Article
YOLOv8-LMG: An Improved Bearing Defect Detection Algorithm Based on YOLOv8
by Minggao Liu, Ming Zhang, Xinlan Chen, Chunting Zheng and Haifeng Wang
Processes 2024, 12(5), 930; https://doi.org/10.3390/pr12050930 - 2 May 2024
Cited by 3 | Viewed by 2280
Abstract
In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing [...] Read more.
In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing defect detection model, YOLOv8-LMG, which is based on the YOLOv8n framework and incorporates four innovative technologies: the VanillaNet backbone network, the Lion optimizer, the CFP-EVC module, and the Shape-IoU loss function. These enhancements significantly increase detection efficiency and accuracy. YOLOv8-LMG achieves a [email protected] of 86.5% and a [email protected]–0.95 of 57.0% on the test dataset, surpassing the original YOLOv8n model while maintaining low computational complexity. Experimental results reveal that the YOLOv8-LMG model boosts accuracy and efficiency in bearing defect detection, showcasing its significant potential and practical value in advancing industrial inspection technologies. Full article
(This article belongs to the Special Issue Fault Diagnosis Process and Evaluation in Systems Engineering)
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14 pages, 1584 KiB  
Article
Strategies for Software and Hardware Compatibility Testing in Industrial Controllers
by Marcus Rothhaupt, Lucas Vogt and Leon Urbas
Processes 2024, 12(3), 580; https://doi.org/10.3390/pr12030580 - 14 Mar 2024
Viewed by 1538
Abstract
Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in [...] Read more.
Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in an industrial context, compatibility problems within industrial control systems arise more and more frequently. In this publication, a strategy concept for compatibility testing is derived and discussed by means of a literature review and applied research. This four-phase strategy concept identifies incompatibilities between software and hardware components in the industrial control environment and enables test engineers to detect problems at an early stage. By automating the compatibility test on an external I-PC, the test can be run both when new software is installed on the industrial controller and when the controller is restarted. Thus, changes to the components are constantly detected and incompatibilities are avoided. Furthermore, early incompatibility detection can ensure that a system remains permanently operational. Based on a discussion, additional strategies are identified to consolidate the robustness and applicability of the presented concept. Full article
(This article belongs to the Special Issue Fault Diagnosis Process and Evaluation in Systems Engineering)
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