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Processes
Special Issues
Transfer Learning Methods in Equipment Reliability Management
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Transfer Learning Methods in Equipment Reliability Management

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 10348

Special Issue Editors

Dr. Lei Wang

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: industrial internet; quality control; data science
Dr. Xin Zhang

E-Mail Website
Guest Editor
Department of mechanical and aerospace engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China
Interests: intelligent fault diagnosis; digital twin; AI for science
Special Issues, Collections and Topics in MDPI journals
Dr. Xixing Li

E-Mail Website
Guest Editor
School of Mechanical Engineering, Hubei University of Technology, Wuhan 430070, China
Interests: production scheduling and optimization, optimization algorithm, manufacturing digitization and informatization

Special Issue Information

Dear Colleagues,

Equipment reliability management is a critical issue in the engineering field, particularly concerning how to ensure that equipment maintains good performance and reliability throughout its entire life cycle. In recent years, with the rapid development of industrial automation and smart manufacturing, collection and analysis of equipment data have become increasingly important. In this context, transfer learning, as an effective machine learning method, has shown great application potential in equipment reliability management. Transfer learning can utilize the knowledge learned from related domains or tasks to help solve problems such as data scarcity and limited computational resources in the target domain, thereby improving the performance of key tasks such as equipment fault diagnosis and predictive maintenance.

Although transfer learning has shown great potential in equipment reliability management, this field still faces some key challenges. For example, due to the differences in structure and working environment between different forms of equipment, how to ensure that the transferred knowledge is relevant and applicable to the target task is a topic that requires in-depth research. Secondly, how to effectively extract and transfer valuable knowledge from source tasks is an urgent problem to be solved. Furthermore, in the specific field of equipment reliability management, how to design efficient transfer learning algorithms and frameworks to fully utilize the advantages of transfer learning is also a worthy research direction. Finally, how to fully integrate transfer learning strategies with reliability analysis techniques such as fault diagnosis and life prediction to achieve more intelligent and comprehensive decision support, while ensuring the generality and practicality of the methods, still requires extensive framework research and application verification.

This Special Issue aims to provide a comprehensive platform for researchers, engineers, and industry practitioners to share their methods or experiences in applying transfer learning to solve equipment reliability problems. We hope that this Special Issue can generate concentrated and consistent contributions in the following areas, including, but not limited to, the following:

  • Domain adaptation technology;
  • Domain generalization technology;
  • Few-shot fault diagnosis and prediction methods;
  • Zero-shot fault diagnosis and prediction methods;
  • Interpretability model;
  • Federated learning and transfer learning;
  • Meta-learning and transfer learning;
  • Other research about transfer learning and equipment reliability managemen

Dr. Lei Wang
Dr. Xin Zhang
Dr. Xixing Li
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

  • equipment reliability
  • transfer learning
  • domain adaptation
  • domain generalization
  • interpretability
  • fault diagnosis
  • fault prediction

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Published Papers (9 papers)

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Research

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19 pages, 6113 KiB  
Article
Research on Lightweight Citrus Leaf Pest and Disease Detection Based on PEW-YOLO
by Renzheng Xue and Luqi Wang
Processes 2025, 13(5), 1365; https://doi.org/10.3390/pr13051365 - 29 Apr 2025
Abstract
Timely detection and prevention of citrus leaf diseases and pests are crucial for improving citrus yield. To address the issue of low efficiency in citrus disease and pest detection, this paper proposes a lightweight detection model named PEW-YOLO. First, the PP-LCNet backbone is [...] Read more.
Timely detection and prevention of citrus leaf diseases and pests are crucial for improving citrus yield. To address the issue of low efficiency in citrus disease and pest detection, this paper proposes a lightweight detection model named PEW-YOLO. First, the PP-LCNet backbone is optimized using a novel GSConv convolution, and a lightweight PGNet backbone is introduced to reduce model parameters while enhancing detection performance. Next, the C2f_EMA module, which integrates efficient multi-scale attention (EMA), replaces the original C2f module in the neck, thereby improving feature fusion capabilities. Finally, the Wise-IoU loss function is employed to address the challenge of identifying low-quality samples, further improving both convergence speed and detection accuracy. Experimental results demonstrate that PEW-YOLO achieves a 1.8% increase in mAP50, a 32.2% reduction in parameters, and a detection speed of 1.6 milliseconds per frame on the citrus disease and pest dataset, thereby meeting practical real-time detection requirements. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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25 pages, 1392 KiB  
Article
Dynamic Scheduling for Multi-Objective Flexible Job Shops with Machine Breakdown by Deep Reinforcement Learning
by Rui Wu, Jianxin Zheng and Xiyan Yin
Processes 2025, 13(4), 1246; https://doi.org/10.3390/pr13041246 - 20 Apr 2025
Viewed by 92
Abstract
Dynamic scheduling for flexible job shops under machine breakdown is a complex and challenging problem due to its valuable application in real-life productions. However, prior studies have struggled to perform well in changeable scenarios. To address this challenge, this paper introduces a dual-objective [...] Read more.
Dynamic scheduling for flexible job shops under machine breakdown is a complex and challenging problem due to its valuable application in real-life productions. However, prior studies have struggled to perform well in changeable scenarios. To address this challenge, this paper introduces a dual-objective deep reinforcement learning (DRL) to solve this problem. This algorithm is based on the Double Deep Q-network (DDQN) and incorporates the attention mechanism. It decouples action relationships in the action space to reduce problem dimensionality and introduces an adaptive weighting method in agent decision-making to obtain high-quality Pareto front solutions. The algorithm is evaluated on a set of benchmark instances and compared with state-of-the-art algorithms. The experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms regarding machine offset and total tardiness, demonstrating more excellent stability and higher-quality solutions. At the same time, the actual use of the algorithm is verified using cases from real enterprises, and the results are still better than those of the multi-objective meta-heuristic algorithm. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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13 pages, 2215 KiB  
Article
Disease Infection Classification in Coconut Tree Based on an Enhanced Visual Geometry Group Model
by Xiaocun Huang, Mustafa Muwafak Alobaedy, Yousef Fazea, S. B. Goyal and Zilong Deng
Processes 2025, 13(3), 689; https://doi.org/10.3390/pr13030689 - 27 Feb 2025
Viewed by 579
Abstract
The coconut is a perennial, evergreen tree in the palm family that belongs to the monocotyledonous group. The coconut plant holds significant economic value due to the diverse functions served by each of its components. Any ailment that impacts the productivity of the [...] Read more.
The coconut is a perennial, evergreen tree in the palm family that belongs to the monocotyledonous group. The coconut plant holds significant economic value due to the diverse functions served by each of its components. Any ailment that impacts the productivity of the coconut plantation will ultimately have repercussions on the associated industries and the sustenance of the families reliant on the coconut economy. Deep learning has the potential to significantly alter the landscape of plant disease detection. Convolutional neural networks are trained using extensive datasets that include annotated images of plant diseases. This training enables the models to develop high-level proficiency in identifying complex patterns and extracting disease-specific features with exceptional accuracy. To address the need for a large dataset for training, an Enhanced Visual Geometry Group (EVGG16) model utilizing transfer learning was developed for detecting disease infections in coconut trees. The EVGG16 model achieves effective training with a limited quantity of data, utilizing the weight parameters of the convolution layer and pooling layer from the pre-training model to perform transfer Visual Geometry Group (VGG16) network model. Through hyperparameter tuning and optimized training batch configurations, we achieved enhanced recognition accuracy, facilitating the development of more robust and stable predictive models. Experimental results demonstrate that the EVGG16 model achieved a 97.70% accuracy rate, highlighting its strong performance and suitability for practical applications in disease detection for plantations. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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14 pages, 5701 KiB  
Article
Finite Element Modeling-Assisted Deep Subdomain Adaptation Method for Tool Condition Monitoring
by Cong Jing, Xin He, Guichang Xu, Luyang Li and Yunfeng Yao
Processes 2025, 13(2), 545; https://doi.org/10.3390/pr13020545 - 15 Feb 2025
Viewed by 387
Abstract
To reduce the experimental costs associated with tool condition monitoring (TCM) under new cutting conditions, a finite element modeling (FEM)-assisted deep subdomain adaptive network (DSAN) approach is proposed. Initially, an FEM technique is employed to construct a cutting tool model for the new [...] Read more.
To reduce the experimental costs associated with tool condition monitoring (TCM) under new cutting conditions, a finite element modeling (FEM)-assisted deep subdomain adaptive network (DSAN) approach is proposed. Initially, an FEM technique is employed to construct a cutting tool model for the new cutting condition (target domain), and the similarity between simulated and experimental data is assessed to obtain valid simulated samples for the target domain. Subsequently, the time–frequency Markov representation method is utilized to extract imaging features from the simulated samples, which serve as input features for the monitoring model. Then, a DSAN model is established to facilitate the transfer from simulation to reality, with the source domain comprising a simulated sample set under new cutting conditions that includes various types of tool conditions obtained through FEM, and the target domain containing only a limited number of normal tool condition samples under new cutting conditions. The application analysis has demonstrated the effectiveness of the proposed method, achieving a classification accuracy of 99%. The proposed approach can significantly reduce experimental costs and obtain high-precision diagnostics of tool conditions with a small sample size. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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28 pages, 5404 KiB  
Article
A Two-Stage Multi-Objective Evolutionary Algorithm for the Dual-Resource Constrained Flexible Job Shop Scheduling Problem with Variable Sublots
by Zekun Huang, Shunsheng Guo, Jinbo Zhang, Guangqiang Bao, Jinshan Yang and Lei Wang
Processes 2025, 13(2), 487; https://doi.org/10.3390/pr13020487 - 10 Feb 2025
Viewed by 678
Abstract
The dual-resource constrained flexible job shop scheduling problem with variable sublots (DRCFJSP-VS) can be decomposed into four subproblems: the sublot splitting subproblem, the sublot sequencing subproblem, the machine assignment subproblem, and the worker assignment subproblem, which are difficult to solve efficiently using conventional [...] Read more.
The dual-resource constrained flexible job shop scheduling problem with variable sublots (DRCFJSP-VS) can be decomposed into four subproblems: the sublot splitting subproblem, the sublot sequencing subproblem, the machine assignment subproblem, and the worker assignment subproblem, which are difficult to solve efficiently using conventional methods. The introduction of variable-size batch splitting and the constraints of multiple levels and skills of workers further increase the complexity of the problem, making it difficult to solve efficiently using conventional methods. This paper proposes a mixed-integer linear programming (MILP) model to solve this complex problem and introduces a two-stage multi-objective evolutionary algorithm (TSMOEA). In the first stage of the algorithm, an improved multi-objective discrete difference evolutionary algorithm is used to optimize the dual-resource constrained flexible job shop scheduling problem; in the second stage, an adaptive simulated annealing algorithm is used to search for variable-size batch splitting strategies. To validate the feasibility of the model, the solution results are obtained using the CPLEX solver and compared with the results of TSMOEA. The performance of TSMOEA is compared with NSGA-II, PSO, DGWO, and WOA on improved instances. The results show that TSMOEA outperforms the other algorithms in both IGD and HV metrics, demonstrating its superior solution quality and robustness. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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19 pages, 4324 KiB  
Article
Research on the Construction Method of an Assembly Knowledge Graph for a Biomass Heating System
by Zuobin Chen, Fukun Wang, Yong Gao, Jia Ai and Ya Mao
Processes 2025, 13(1), 11; https://doi.org/10.3390/pr13010011 - 24 Dec 2024
Viewed by 771
Abstract
In the complex process of assembling biomass heating systems, traditional paper documents and construction process card management methods have weak information correlation and take a long time for information retrieval, which seriously restricts the assembly efficiency and quality. Moreover, the assembly process involves [...] Read more.
In the complex process of assembling biomass heating systems, traditional paper documents and construction process card management methods have weak information correlation and take a long time for information retrieval, which seriously restricts the assembly efficiency and quality. Moreover, the assembly process involves numerous components and complex processes, making it difficult for traditional management methods to cope with. To address this issue, a knowledge graph-based assembly information integration method is proposed to integrate scattered assembly information into a graph database, providing pathways for accessing assembly information and assisting on-site management. The biomass heating system assembly knowledge graph (BAKG) adopts the top-down method construction. After the construction of the upper schema layer, the 3DXML file was parsed, the XML.dom parser in Python3.7.16 was used to extract the equipment structure information, and the RoBERTa-BiLSTM-CRF model was applied to the named entity recognition of the assembly document, which improved the accuracy of entity recognition. The experimental results show that the F1 score of the RoBERTa-BiLSTM-CRF model in entity recognition during the assembly process reaches 92.19%, which is 3.1% higher than that of the traditional BERT-BiLSTM-CRF model. Moreover, the knowledge graph structure generated by the equipment structure data based on 3DXML file is similar to the equipment structure tree, but is more clear and intuitive. Finally, taking the second-phase construction process records of a company as an example, BAKG was constructed and assembly information was stored in the Neo4j graph database in the form of graphs, which verified the effectiveness of the method. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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26 pages, 11849 KiB  
Article
The Identification, Separation, and Clamp Function of an Intelligent Flexible Blueberry Picking Robot
by Xiaohong Liu, Peifu Li, Bo Hu, Hao Yin, Zexian Wang, Wenxin Li, Yanxia Xu and Baogang Li
Processes 2024, 12(11), 2591; https://doi.org/10.3390/pr12112591 - 18 Nov 2024
Viewed by 1022
Abstract
Identifying fruit maturity accurately and achieving damage-free harvesting are challenges in designing blueberry-picking robots. This paper presents an intelligent flexible picking system. First, we trained a deep learning-based YOLOv8n network to locate the position of the fruit and determine fruit ripeness. We used [...] Read more.
Identifying fruit maturity accurately and achieving damage-free harvesting are challenges in designing blueberry-picking robots. This paper presents an intelligent flexible picking system. First, we trained a deep learning-based YOLOv8n network to locate the position of the fruit and determine fruit ripeness. We used a neural network to establish the relationship between fruit hardness and shape parameters, achieving an adaptive gripping force for different fruits. To address the issue of dense clusters in some blueberry varieties, we designed a fruit separation subsystem using a combination of flow field analysis and pressure-sensitive experiments. The results show that the mean average precision can reach 84.62%, the precision is 94.49%, the recall is 83.85%, the F1 score is 88.85%, and the test time is 0.12 s, which can meet the requirements for blueberry fruit recognition accuracy and speed. The spacing between closely packed fruits can increase by 4 mm, and the damage-free picking rate exceeds 92%, achieving stable, damage-free harvesting. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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18 pages, 3253 KiB  
Article
Application of Machine Learning for the Prediction of Absorption, Distribution, Metabolism and Excretion (ADME) Properties from Cichorium intybus Plant Phytomolecules
by Ayesha Hena Afzal, Ozair Alam, Sherin Zafar, Mohammad Afshar Alam, Kahksha Ahmed, Jalaluddin Khan, Rahmuddin Khan, Abdelaaty A. Shahat and Abdulsalam Alhalmi
Processes 2024, 12(11), 2488; https://doi.org/10.3390/pr12112488 - 8 Nov 2024
Cited by 2 | Viewed by 1374
Abstract
Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural [...] Read more.
Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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Review

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47 pages, 1743 KiB  
Review
Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review
by Yo-Ping Huang and Simon Peter Khabusi
Processes 2025, 13(1), 73; https://doi.org/10.3390/pr13010073 - 1 Jan 2025
Cited by 4 | Viewed by 5257
Abstract
The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the aquaculture industry, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. This review explores the [...] Read more.
The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the aquaculture industry, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. This review explores the latest research studies in AIoT within the aquaculture industry, focusing on real-time environmental monitoring, data-driven decision-making, and automation. IoT sensors deployed across aquaculture systems continuously track critical parameters such as temperature, pH, dissolved oxygen, salinity, and fish behavior. AI algorithms process these data streams to provide predictive insights into water quality management, disease detection, species identification, biomass estimation, and optimized feeding strategies, among others. Much as AIoT adoption in aquaculture is advantageous on various fronts, there are still numerous challenges, including high implementation costs, data privacy concerns, and the need for scalable and adaptable AI models across diverse aquaculture environments. This review also highlights future directions for AIoT in aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management. Full article
(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)
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