AI and Data-Driven Advancements in Industry 4.0, 2nd Edition

Topic Information

Dear Colleagues,

Our society is awash with diverse forms of data—pictures, point clouds, text, audio, video, and beyond. Data-driven artificial intelligence offers the potential to extract meaningful insights from this deluge, unlocking extraordinary opportunities in both theory and application. Recent years have witnessed a proliferation of AI theories and algorithms, now trusted and employed across sectors like finance, security, education, art, neuroscience, and even music. Inspired by these advancements, substantial AI-based techniques are being implemented in fields as varied as autonomous driving, virtual reality, human–computer interaction, remote sensing, and artistic creation.

This focus area seeks to collect and highlight the latest breakthroughs in next-generation artificial intelligence and its applications within the context of Industry 4.0. Our interest spans the full spectrum of AI research, from compact on-device models to expansive large-scale models, across diverse domains. We are particularly keen on sophisticated framework designs, training strategies, optimization techniques, and ensuring the trustworthiness and robustness of AI systems, along with their real-world applications.

Topics include (but are not limited to) the following:

• Computer vision, natural language processing, reinforcement learning;
• Large-scale language model, large-scale vision model, prompt learning, retrieval-augmented generation;
• Multi-modal learning, object recognition, detection, segmentation, tracking;
• Graph neural network, knowledge graph, recommendation system;
• Pattern recognition and intelligent system;
• Blockchain theory or application, smart contract;
• Artificial intelligence security, data security and privacy;
• Remote sensing image interpretation;
• Art generation and creation, art analysis and understanding;
• Virtual reality, robotics, edge computing, on-device models;
• Autonomous driving.

Dr. Teng Huang
Dr. Yan Pang
Prof. Dr. Qiong Wang
Prof. Dr. Jianjun Li
Dr. Jin Liu
Dr. Jia Wang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

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Published Papers (8 papers)

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30 pages, 5391 KiB  

Open AccessArticle

Dual-Resource Scheduling with Improved Forensic-Based Investigation Algorithm in Smart Manufacturing

by Yuhang Zeng, Ping Lou, Jianmin Hu, Chuannian Fan, Quan Liu and Jiwei Hu

Mathematics 2025, 13(9), 1432; https://doi.org/10.3390/math13091432 - 27 Apr 2025

Viewed by 194

Abstract

With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, [...] Read more.

With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. In addition, a two-stage approach based on a forensic-based investigation (TSFBI) is proposed to solve the problem. In the first stage, an improved multi-objective FBI algorithm is used to obtain the Pareto front solutions of this model, in which a hybrid real and integer encoding–decoding method is used for exploring the solution space and a fast non-dominated sorting method for improving efficiency. In the second stage, a multi-criteria decision analysis method based on an analytic hierarchy process (AHP) is used to select the optimal solution from the Pareto front solutions. Finally, experiments validated the TSFBI algorithm, showing its potential for smart manufacturing. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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21 pages, 9224 KiB  

Open AccessFeature PaperArticle

A Multi-Scale Fusion Convolutional Network for Time-Series Silicon Prediction in Blast Furnaces

by Qiancheng Hao, Wenjing Liu, Wenze Gao and Xianpeng Wang

Mathematics 2025, 13(8), 1347; https://doi.org/10.3390/math13081347 - 20 Apr 2025

Viewed by 126

Abstract

In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly reflected by the silicon content in the hot [...] Read more.

In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly reflected by the silicon content in the hot metal. However, due to the dynamic nature and inherent delays of the ironmaking process, real-time prediction of silicon content remains a significant challenge, and traditional methods often suffer from insufficient prediction accuracy. This study presents a novel Multi-Scale Fusion Convolutional Neural Network (MSF-CNN) to accurately predict the silicon content during the blast furnace smelting process, addressing the limitations of existing data-driven approaches. The proposed MSF-CNN model extracts temporal features at two distinct scales. The first scale utilizes a Convolutional Block Attention Module, which captures local temporal dependencies by focusing on the most relevant features across adjacent time steps. The second scale employs a Multi-Head Self-Attention mechanism to model long-term temporal dependencies, overcoming the inherent delay issues in the blast furnace process. By combining these two scales, the model effectively captures both short-term and long-term temporal dependencies, thereby enhancing prediction accuracy and real-time applicability. Validation using real blast furnace data demonstrates that MSF-CNN outperforms recurrent neural network models such as Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). Compared with LSTM and the GRU, MSF-CNN reduces the Root Mean Square Error (RMSE) by approximately 22% and 21%, respectively, and improves the Hit Rate (HR) by over 3.5% and 4%, highlighting its superiority in capturing complex temporal dependencies. These results indicate that the MSF-CNN adapts better to the blast furnace’s dynamic variations and inherent delays, achieving significant improvements in prediction precision and robustness compared to state-of-the-art recurrent models. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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25 pages, 8768 KiB  

Open AccessArticle

Towards More Accurate Industrial Anomaly Detection: A Component-Level Feature-Enhancement Approach

by Xiaodong Wang, Zhiyao Xie, Fei Yan, Jiayu Wang, Jiangtao Fan, Zhiqiang Zeng, Junwen Lu, Hangqi Zhang and Nianfeng Zeng

Electronics 2025, 14(8), 1613; https://doi.org/10.3390/electronics14081613 - 16 Apr 2025

Viewed by 245

Abstract

Industrial visual inspection plays a crucial role in intelligent manufacturing. However, existing anomaly-detection methods based on unsupervised learning paradigms often struggle with issues such as overlooking minor defects and blurring component edges in confidence maps. To address these challenges, this paper proposes an [...] Read more.

Industrial visual inspection plays a crucial role in intelligent manufacturing. However, existing anomaly-detection methods based on unsupervised learning paradigms often struggle with issues such as overlooking minor defects and blurring component edges in confidence maps. To address these challenges, this paper proposes an industrial anomaly-detection method based on component-level feature enhancement. This method introduces a component-level feature-enhancement module, which optimizes feature matching by calculating the structural similarity between global coarse-grained confidence features and local fine-grained confidence features, thereby generating enhanced feature maps to improve the model’s detection accuracy for minor defects and local anomalies. Additionally, we propose a region-segmentation method based on multi-layer piecewise thresholds, which effectively distinguishes between foreground and background in confidence maps, circumvents background interference and ensures the integrity of structural information of foreground components. Experimental results demonstrate that the proposed method surpasses comparative methods in both logical and structural defect detection tasks, showing significant advantages, especially in fine-grained anomaly detection, with stronger robustness and accuracy. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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23 pages, 3749 KiB  

Open AccessFeature PaperArticle

Proposed Long Short-Term Memory Model Utilizing Multiple Strands for Enhanced Forecasting and Classification of Sensory Measurements

by Sotirios Kontogiannis, George Kokkonis and Christos Pikridas

Mathematics 2025, 13(8), 1263; https://doi.org/10.3390/math13081263 - 11 Apr 2025

Viewed by 203

Abstract

This paper presents a new deep learning model called the stranded Long Short-Term Memory. The model utilizes arbitrary LSTM recurrent neural networks of variable cell depths organized in classes. The proposed model can adapt to classifying emergencies at different intervals or provide measurement [...] Read more.

This paper presents a new deep learning model called the stranded Long Short-Term Memory. The model utilizes arbitrary LSTM recurrent neural networks of variable cell depths organized in classes. The proposed model can adapt to classifying emergencies at different intervals or provide measurement predictions using class-annotated or time-shifted series of sensory data inputs. In order to outperform the ordinary LSTM model’s classifications or forecasts by minimizing losses, stranded LSTM maintains three different weight-based strategies that can be arbitrarily selected prior to model training, as follows: least loss, weighted least loss, and fuzzy least loss in the LSTM model selection and inference process. The model has been tested against LSTM models for forecasting and classification, using a time series of temperature and humidity measurements taken from meteorological stations and class-annotated temperature measurements from Industrial compressors accordingly. From the experimental classification results, the stranded LSTM model outperformed 0.9–2.3% of the LSTM models carrying dual-stacked LSTM cells in terms of accuracy. Regarding the forecasting experimental results, the forecast aggregation weighted and fuzzy least loss strategies performed 5–7% better, with less loss, using the selected LSTM model strands supported by the model’s least loss strategy. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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21 pages, 20129 KiB  

Open AccessArticle

UMAP-Based All-MLP Marine Diesel Engine Fault Detection Method

by Shengli Dong, Jilong Liu, Bing Han, Shengzheng Wang, Hong Zeng and Meng Zhang

Electronics 2025, 14(7), 1293; https://doi.org/10.3390/electronics14071293 - 25 Mar 2025

Viewed by 255

Abstract

This study presents an innovative approach for marine diesel engine fault detection, integrating unsupervised learning through Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction with time series prediction, offering significant improvements over existing methods. Unlike traditional model-based or expert-driven approaches, which struggle with [...] Read more.

This study presents an innovative approach for marine diesel engine fault detection, integrating unsupervised learning through Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction with time series prediction, offering significant improvements over existing methods. Unlike traditional model-based or expert-driven approaches, which struggle with complex nonlinear systems, or supervised data-driven methods limited by scarce labeled fault data, our unsupervised method establishes a normal operational baseline without requiring fault labels, enhancing applicability across diverse conditions. Leveraging UMAP’s nonlinear dimensionality reduction, the proposed method outperforms conventional linear techniques (e.g., PCA) by amplifying subtle system anomalies, enabling earlier detection of state transitions—up to two batches before deviations appear in traditional performance indicators (Ps)—thus improving fault detection sensitivity. To address nonlinear relationships in UMAP-reduced dimensions, the proposed TimeMixer-FI model enhances the TimeMixer architecture with MLP-Mixer layers. The TimeMixer-FI model demonstrates consistent improvements over the original TimeMixer across various sequence lengths, achieving an MSE reduction of 69.1% (from 0.0544 to 0.0168) and an MAE reduction of 46.3% (from 0.1023 to 0.0549) at an input sequence length of 60 time steps, thereby enhancing the reliability of the time series prediction baseline. Experimental results validate that this approach significantly enhances both the sensitivity and accuracy of early fault detection, providing a more robust and efficient solution for predictive maintenance in marine diesel engines. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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21 pages, 2803 KiB  

Open AccessArticle

Flexible Capacitated Vehicle Routing Problem Solution Method Based on Memory Pointer Network

by Enliang Wang, Yue Cai and Zhixin Sun

Mathematics 2025, 13(7), 1061; https://doi.org/10.3390/math13071061 - 25 Mar 2025

Viewed by 271

Abstract

In real-world logistics scenarios, the complexities often surpass what traditional Capacitated Vehicle Routing Problem (CVRP) models can effectively address. For instance, when there is an excess of goods and limited vehicles, traditional CVRP models frequently fail to yield feasible solutions. Additionally, the time [...] Read more.

In real-world logistics scenarios, the complexities often surpass what traditional Capacitated Vehicle Routing Problem (CVRP) models can effectively address. For instance, when there is an excess of goods and limited vehicles, traditional CVRP models frequently fail to yield feasible solutions. Additionally, the time sensitivity of goods and the large scale of vehicles and goods in practical logistics scenarios present significant challenges for efficient problem-solving. This underscores the urgent need to develop a novel CVRP model that is better suited for logistics scenarios and enhances the scalability of CVRP. To address these limitations, we propose a flexible CVRP model, referred to as Flexible CVRP, which modifies the optimization objectives and constraints. This allows CVRP to provide a sensible solution even when no feasible solution exists in the traditional sense. To tackle the challenges posed by large-scale problems, we leverage the Memory Pointer Network (MemPtrN). This approach enables the modeling of solution strategies, offering strong generalization capabilities and mitigating the explosive growth in complexity to some extent. Compared to commonly used heuristic algorithms, our method achieves superior solution quality for large-scale problems. Specifically, when addressing large-scale scenarios, the MemPtrN outperforms Google’s OR-Tools solver, heuristic algorithms, enhanced evolutionary algorithms, and other reinforcement learning methods in terms of both solution speed and quality. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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20 pages, 12008 KiB  

Open AccessArticle

Artificial Intelligence-Based Fault Diagnosis for Steam Traps Using Statistical Time Series Features and a Transformer Encoder-Decoder Model

by Chul Kim, Kwangjae Cho and Inwhee Joe

Electronics 2025, 14(5), 1010; https://doi.org/10.3390/electronics14051010 - 3 Mar 2025

Cited by 1 | Viewed by 818

Abstract

Steam traps are essential for industrial systems, ensuring steam quality and energy efficiency by removing condensate and preventing steam leakage. However, their failure results in energy loss, operational disruptions, and increased greenhouse gas emissions. This paper proposes a novel predictive maintenance system for [...] Read more.

Steam traps are essential for industrial systems, ensuring steam quality and energy efficiency by removing condensate and preventing steam leakage. However, their failure results in energy loss, operational disruptions, and increased greenhouse gas emissions. This paper proposes a novel predictive maintenance system for steam traps that integrates statistical time series features and transformer encoder–decoder models for fault diagnosis and visualization. The proposed system combines IoT sensor data, operational parameters, open data (e.g., weather information and public holiday calendars), machine learning, and two-dimensional diagnostic projection to improve reliability and interpretability. Experiments were conducted in two industrial plants: an aluminum processing plant and a food manufacturing plant, and the system achieved superior defect detection accuracy and diagnostic reliability compared to existing methods. The transformer-based model outperformed traditional methods, including random forest, gradient boosting, and variational autoencoder, in classification and clustering. The system also demonstrated an average 6.92% reduction in thermal energy across both sites, highlighting its potential to improve energy efficiency and reduce carbon emissions. This research highlights the transformative impact of AI-based predictive maintenance technologies in industrial operations and provides a framework for sustainable manufacturing practices. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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21 pages, 2600 KiB  

Open AccessArticle

A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples

by Jiasheng Yan, Yang Sui and Tao Dai

Mathematics 2025, 13(5), 797; https://doi.org/10.3390/math13050797 - 27 Feb 2025

Viewed by 409

Abstract

Intelligent fault diagnosis (IFD) plays a crucial role in reducing maintenance costs and enhancing the reliability of safety-critical energy systems (SCESs). In recent years, deep learning-based IFD methods have achieved high fault diagnosis accuracy extracting implicit higher-order correlations between features. However, the excessive [...] Read more.

Intelligent fault diagnosis (IFD) plays a crucial role in reducing maintenance costs and enhancing the reliability of safety-critical energy systems (SCESs). In recent years, deep learning-based IFD methods have achieved high fault diagnosis accuracy extracting implicit higher-order correlations between features. However, the excessive long training time of deep learning models conflicts with the requirements of real-time analysis for IFD, hindering their further application in practical industrial environments. To address the aforementioned challenge, this paper proposes an innovative IFD method for SCES that combines the particle swarm optimization (PSO) algorithm and the ensemble broad learning system (EBLS). Specifically, the broad learning system (BLS), known for its low time complexity and high classification accuracy, is adopted as an alternative to deep learning for fault diagnosis in SCES. Furthermore, EBLS is designed to enhance model stability and classification accuracy with high-dimensional small samples by incorporating the random forest (RF) algorithm and an ensemble strategy into the traditional BLS framework. In order to reduce the computational cost of the EBLS, which is constrained by the selection of its hyperparameters, the PSO algorithm is employed to optimize the hyperparameters of the EBLS. Finally, the model is validated through simulated data from a complex nuclear power plant (NPP). Numerical experiments reveal that the proposed method significantly improved the diagnostic efficiency while maintaining high accuracy. In summary, the proposed approach shows great promise for boosting the capabilities of the IFD models for SCES. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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