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Processes
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Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry...
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Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 4845

Special Issue Editors

Dr. Yang Li

E-Mail Website
Guest Editor
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Interests: testability design; data-driven-based fault detection and isolation (FDI); system control and optimization; PHM
Special Issues, Collections and Topics in MDPI journals
Dr. Cunsong Wang

E-Mail Website
Guest Editor
Institute of Intelligent Manufacturing, Nanjing Tech University, Nanjing 210009, China
Interests: intelligent manufacturing; intelligent control and systems; intelligent monitoring and fault diagnosis; prognostic and health management
Dr. Yan Shi

E-Mail Website
Guest Editor
Institute for Risk and Reliability, Leibniz University Hannover, Callinstr. 34, 30167 Hannover, Germany
Interests: hybrid uncertainties; decision-making for complex networks; machine learning
Prof. Dr. Li Jia

E-Mail Website
Guest Editor
Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China
Interests: data-driven complex nonlinear dynamic modelling; integrated learning optimization control of intermittent production processes; energy internet collaborative optimization based on big data

Special Issue Information

Dear Colleagues,

As the industrial sector progresses toward Industry 5.0, intelligent manufacturing systems’ modeling, design, optimization, and maintenance have garnered increasing scholarly attention. This evolution emphasizes the integration of advanced cyber–physical systems, artificial intelligence, and human-centric technologies to foster sustainability, efficiency, and resilience in production processes. Central to this paradigm shift are the principles of process modeling, multi-objective optimization, and predictive maintenance, which are applied across various scales—from individual unit operations to entire manufacturing ecosystems. These methodologies aim to enhance resource utilization, minimize energy consumption, and improve system adaptability. Industry 5.0 represents a convergence of human ingenuity and machine precision, leading to a synergistic enhancement of manufacturing capabilities that transcends the limitations of traditional industrial frameworks.

This Special Issue entitled “Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0” seeks high-quality works focusing on the latest novel advancements in technology for intelligent manufacturing systems. Topics include, but are not limited to, methods and/or application in the following areas:

  • Smart manufacturing;
  • Industrial fault diagnostics and prognosis;
  • Predictive maintenance;
  • Industrial reliability assessment;
  • AI-driven optimization;
  • Digital twins;
  • Human–AI collaboration;
  • Industrial IoT;
  • Data-driven decision-making.

Dr. Yang Li
Dr. Cunsong Wang
Dr. Yan Shi
Prof. Dr. Li Jia
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

  • intelligent monitoring and maintenance
  • prognostic and health management
  • reliability assessment
  • online soft measurement
  • intelligent management and control
  • Industrial Internet of Things

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

Published Papers (6 papers)

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Research

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28 pages, 74259 KiB  
Article
Comparative Analysis of Binarization Approaches for Automated Dye Penetrant Testing
by Peter Josef Haupts, Hammoud Al-Joumaa, Loui Al-Shrouf and Mohieddine Jelali
Processes 2025, 13(4), 1212; https://doi.org/10.3390/pr13041212 - 16 Apr 2025
Viewed by 191
Abstract
This paper presents a comparative study of binarization techniques for automated defect detection in dye penetrant testing (DPT) images. We evaluate established methods, including global, adaptive, and histogram-based thresholding, against three novel machine learning-assisted approaches, Soft Binarization (SoBin), Delta Binarization (DeBin), and Convolutional [...] Read more.
This paper presents a comparative study of binarization techniques for automated defect detection in dye penetrant testing (DPT) images. We evaluate established methods, including global, adaptive, and histogram-based thresholding, against three novel machine learning-assisted approaches, Soft Binarization (SoBin), Delta Binarization (DeBin), and Convolutional Autoencoder Binarization (AutoBin), using a real-world dataset from an automated DPT system inspecting stainless steel pipes. Performance is assessed with both pixel-level and region-level metrics, with particular emphasis on the influence of defect saturation. Defect saturation is quantified as the mean saturation value of all pixels belonging to a given defect, and defects are grouped into ten categories spanning from low (60–68) to high (132–140) mean saturation. Our results demonstrate that for lower mean defect saturation values, methods such as AutoBin_Triangle, HSV_global_70, and SoBin achieve superior Intersection over Union (IoU) and high true positive rates. In contrast, methods based primarily on global thresholding of the saturation channel tend to perform competitively on images with higher defect saturation levels, reflecting their sensitivity to stronger color signals. Moreover, depending on the method, nearly perfect region-level true positive rates (TPRregion) or minimal false positive rates (FPRregion) can be attained, emphasizing the trade-off that different models offer distinct strengths and weaknesses, which necessitates selecting the optimal method based on the specific quality control requirements and risk tolerances of the industrial process. These findings underscore the critical importance of defect saturation as a cue for both human and computer vision systems and provide valuable insights for developing robust automated quality control and predictive quality algorithms. Full article
(This article belongs to the Special Issue Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0)
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17 pages, 15727 KiB  
Article
Comparative Study of Multiple-Sensor-Fault-Detection Based Time–Frequency Analysis Methods on Lithium-Ion Batteries
by Qiancheng Wang, Hui Chen and Engang Tian
Processes 2025, 13(4), 929; https://doi.org/10.3390/pr13040929 - 21 Mar 2025
Viewed by 310
Abstract
Rapid multi-sensor fault detection is crucial for the battery management system (BMS). Almost all the existing fault diagnosis methods for current sensors are model-based, and the complexity of the models poses a huge challenge to their application in engineering. Firstly, this paper conducts [...] Read more.
Rapid multi-sensor fault detection is crucial for the battery management system (BMS). Almost all the existing fault diagnosis methods for current sensors are model-based, and the complexity of the models poses a huge challenge to their application in engineering. Firstly, this paper conducts a detailed analysis of the physical meanings of six forms of sensor faults, and these six types of faults are modeled using mathematical methods. To better compare the detection ability of each method for different faults, these faults are standardized during the modeling. Then, the characteristics of five existing time–frequency analysis methods are analyzed. Finally, a multi-window short-time Fourier transform (MW-STFT) for lithium-ion battery fault detection is proposed. The experimental results show that the proposed MW-STFT can detect all the sensor faults. Full article
(This article belongs to the Special Issue Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0)
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16 pages, 7170 KiB  
Article
Optimizing Reactive Compensation for Enhanced Voltage Stability in Renewable-Integrated Stochastic Distribution Networks
by Yiguo Guo, Yimu Fu, Jingxuan Li and Jiajia Chen
Processes 2025, 13(2), 303; https://doi.org/10.3390/pr13020303 - 22 Jan 2025
Cited by 1 | Viewed by 691
Abstract
The rapid expansion of renewable energy sources and the increasing electrical load demand are complicating the operational dynamics of power grids, leading to significant voltage fluctuations and elevated line losses. To address these challenges, we propose an information gap decision-theory-based robust optimization method [...] Read more.
The rapid expansion of renewable energy sources and the increasing electrical load demand are complicating the operational dynamics of power grids, leading to significant voltage fluctuations and elevated line losses. To address these challenges, we propose an information gap decision-theory-based robust optimization method for the siting and operation of reactive compensation equipment, utilizing static var generators (SVGs) to mitigate voltage fluctuations and reduce losses. Our approach begins by projecting the scale of renewable energy integration and load growth, establishing scenarios with varying renewable-to-load growth ratios. We then develop a multi-objective optimization model that incorporates voltage–loss sensitivity, accounting for the uncertainties in renewable energy production. A case study demonstrates that our method reduces grid voltage fluctuations and losses by 29.53% and 7.75%, respectively, compared to non-intervention scenarios, highlighting its effectiveness in stabilizing distribution networks. Full article
(This article belongs to the Special Issue Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0)
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19 pages, 3861 KiB  
Article
A Novel Temporal Fusion Channel Network with Multi-Channel Hybrid Attention for the Remaining Useful Life Prediction of Rolling Bearings
by Cunsong Wang, Junjie Jiang, Heng Qi, Dengfeng Zhang and Xiaodong Han
Processes 2024, 12(12), 2762; https://doi.org/10.3390/pr12122762 - 5 Dec 2024
Viewed by 787
Abstract
The remaining useful life (RUL) prediction of rolling bearings is crucial for optimizing maintenance schedules, reducing downtime, and extending machinery lifespan. However, existing multi-channel feature fusion methods do not fully capture the correlations between channels and time points in multi-dimensional sensor data. To [...] Read more.
The remaining useful life (RUL) prediction of rolling bearings is crucial for optimizing maintenance schedules, reducing downtime, and extending machinery lifespan. However, existing multi-channel feature fusion methods do not fully capture the correlations between channels and time points in multi-dimensional sensor data. To address the above problems, this paper proposes a multi-channel feature fusion algorithm based on a hybrid attention mechanism and temporal convolutional networks (TCNs), called MCHA-TFCN. The model employs a dual-channel hybrid attention mechanism, integrating self-attention and channel attention to extract spatiotemporal features from multi-channel inputs. It uses causal dilated convolutions in TCNs to capture long-term dependencies and incorporates enhanced residual structures for global feature fusion, effectively extracting high-level spatiotemporal degradation information. The experimental results on the PHM2012 dataset show that MCHA-TFCN achieves excellent performance, with an average Root-Mean-Square Error (RMSE) of 0.091, significantly outperforming existing methods like the DANN and CNN-LSTM. Full article
(This article belongs to the Special Issue Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0)
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17 pages, 3551 KiB  
Article
Q-Learning-Incorporated Robust Relevance Vector Machine for Remaining Useful Life Prediction
by Xiuli Wang, Zhongxin Li, Xiuyi Wang and Xinyu Hu
Processes 2024, 12(11), 2536; https://doi.org/10.3390/pr12112536 - 13 Nov 2024
Cited by 1 | Viewed by 977
Abstract
Accurate and reliable remaining useful life (RUL) prediction is crucial for improving equipment reliability and safety, realizing predictive maintenance. The relevance vector machine (RVM) method is commonly utilized for RUL prediction, profiting from its sparse property under a Bayesian framework. However, the RVM [...] Read more.
Accurate and reliable remaining useful life (RUL) prediction is crucial for improving equipment reliability and safety, realizing predictive maintenance. The relevance vector machine (RVM) method is commonly utilized for RUL prediction, profiting from its sparse property under a Bayesian framework. However, the RVM faces the issue of poor robustness, which is mainly manifested as poor prediction accuracy and difficulty in fitting when the predicted data fluctuate greatly. This is due to weights and random errors following Gaussian distributions, which are highly sensitive to outliers. Also, the traditional model training process heavily relies on an additional feature extraction process, which suffers from the problem of effective data loss as well as the risk of overfitting. Thus, a robust regression framework against outliers is developed by incorporating t-distribution into the RVM. And a Q-learning (QL) algorithm is embedded into the constructed robust RVM model to replace the feature extraction process. In addition, this paper firstly predicts the degradation trend of RUL to enhance the accuracy and interpretability of RUL prediction. Finally, a comparative experiment on the performance degradation of capacitors in the traction system is designed, and the root mean square errors for the QL-RRVM, QL-RVM, RRVM, and RVM models are obtained as 0.751, 8.599, 38.316, and 41.892, respectively. The experimental results confirm the superiority of the proposed method. Full article
(This article belongs to the Special Issue Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0)
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Review

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19 pages, 1909 KiB  
Review
A Review of Synchronous Fixed-Frequency Microgrid Droop Control Systems Based on Global Positioning System
by Kuan Li, Yudun Li, Rongqi Fan, Zehao Liu and Maozeng Lu
Processes 2025, 13(1), 54; https://doi.org/10.3390/pr13010054 - 30 Dec 2024
Viewed by 996
Abstract
Microgrids, as a new type of power supply network that connects distributed energy sources with power loads, can operate in both grid-connected and islanded states. It has the advantages of high reliability and flexible configuration. When the microgrid operates in islanding mode, ensuring [...] Read more.
Microgrids, as a new type of power supply network that connects distributed energy sources with power loads, can operate in both grid-connected and islanded states. It has the advantages of high reliability and flexible configuration. When the microgrid operates in islanding mode, ensuring voltage and frequency stability becomes a primary focus of research. This paper provides a brief overview of the master-slave control and peer-to-peer control strategies used in microgrids, analyzing the advantages and disadvantages of each approach. The application of droop control strategies to microgrid converters is emphasized. This research analyzes the implementation of droop control strategies in addressing microgrid frequency and power offsets. Given the advantages of the synchronized fixed-frequency droop control method, the authors provide a detailed overview of this strategy, which is based on the global satellite navigation system (GPS). On this basis, a comprehensive comparison of various synchronous frequency control methods is conducted, analyzing the advantages and disadvantages of each approach. Finally, the research findings in this area are summarized, and the future development trends of research in this field are discussed and anticipated. Full article
(This article belongs to the Special Issue Modeling, Design, Optimization and Maintenance of Intelligent Manufacturing Towards Industry 5.0)
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