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Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic...
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Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 4752

Special Issue Editors

Dr. Honggang Chen

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan Li

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: electrical engineering; high-voltage and insulation technology; power transmission and distribution; energy storage
Dr. Junyu Guo

E-Mail Website
Guest Editor
School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Interests: reliability assessments; condition monitoring; fault diagnosis; residual life prediction

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) technologies into the fields of energy, manufacturing, and automatic control processes is transforming these industries by enhancing efficiency, accuracy, and innovation. As the volume and complexity of data grow, AI's role in these sectors becomes increasingly vital.

This Special Issue focuses on showcasing cutting-edge research where AI technologies are applied to optimize energy systems, revolutionize manufacturing processes, and refine automatic control mechanisms. Contributions to this Special Issue will highlight how AI not only improves operational efficiencies but also drives the evolution of these crucial sectors toward a more innovative and sustainable future.

The topics covered may include, but are not limited to, the following:

  • AI for condition monitoring;
  • AI for fault diagnosis;
  • AI for decision support;
  • AI for risk prediction;
  • AI for process management.

Dr. Honggang Chen
Dr. Yuan Li
Dr. Junyu Guo
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

  • artificial intelligence
  • deep learning
  • machine learning
  • energy
  • manufacturing
  • automatic control

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

Published Papers (6 papers)

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Research

19 pages, 3906 KiB  
Article
Adaptive Enhancement of Thermal Infrared Images for High-Voltage Cable Buffer Layer Ablation
by Hao Zhan, Jing Zhang, Yuhao Lan, Fan Zhang, Qinqing Huang, Kai Zhou and Chengde Wan
Processes 2024, 12(11), 2543; https://doi.org/10.3390/pr12112543 - 14 Nov 2024
Viewed by 410
Abstract
In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous [...] Read more.
In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous practice has demonstrated that detecting buffer layer ablation through surface temperature distribution changes is feasible, offering a convenient, efficient, and non-destructive approach. However, the variability in heat generation and the subtle temperature differences in thermal infrared images, compounded by noise interference, can impair the accuracy and timeliness of fault detection. To overcome these challenges, this paper introduces an adaptive enhancement method for the thermal infrared imaging of high-voltage cable buffer layer ablation. The method involves an Average Gradient Weighted Guided Filtering (AGWGF) technique to decompose the image into background and detail layers, preventing noise amplification during enhancement. The background layer, containing the primary information, is enhanced using an improved Contrast Limited Adaptive Histogram Equalization (CLAHE) to accentuate temperature differences. The detail layer, rich in high-frequency content, undergoes improved Adaptive Bilateral Filtering (ABF) for noise reduction. The enhanced background and detail layers are then fused and stretched to produce the final enhanced thermal image. To vividly depict temperature variations in the buffer layer, pseudo-color processing is applied to generate color-infrared thermal images. The results indicate that the proposed method’s enhanced images and pseudo-colored infrared thermal images provide a clearer and more intuitive representation of temperature differences compared to the original images, with an average increase of 2.17 in information entropy and 8.38 in average gradient. This enhancement facilitates the detection and assessment of buffer layer ablation faults, enabling the prompt identification of faults. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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16 pages, 12268 KiB  
Article
Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy
by Zhikun Huang, Jingchao Yan, Jianlong Zhang, Chong Han, Jingfei Peng, Ju Cheng, Zhenggang Wang, Min Luo and Pengbo Yin
Processes 2024, 12(10), 2214; https://doi.org/10.3390/pr12102214 - 11 Oct 2024
Viewed by 638
Abstract
As industrial development drives the increasing demand for steel, accurate estimation of the material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly [...] Read more.
As industrial development drives the increasing demand for steel, accurate estimation of the material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly and time-consuming, and fatigue failure can lead to severe consequences. Therefore, the need to develop faster and more efficient methods for predicting fatigue strength is evident. In this paper, a fatigue strength dataset was established, incorporating data on material element composition, physical properties, and mechanical performance parameters that influence fatigue strength. A machine learning regression model was then applied to facilitate rapid and efficient fatigue strength prediction of ferrous alloys. Twenty characteristic parameters, selected for their practical relevance in engineering applications, were used as input variables, with fatigue strength as the output. Multiple algorithms were trained on the dataset, and a deep learning regression model was employed for the prediction of fatigue strength. The performance of the models was evaluated using metrics such as MAE, RMSE, R2, and MAPE. The results demonstrated the superiority of the proposed models and the effectiveness of the applied methodologies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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23 pages, 21133 KiB  
Article
Data-Driven Feature Extraction-Transformer: A Hybrid Fault Diagnosis Scheme Utilizing Acoustic Emission Signals
by Chenggong Ma, Jiuyang Gao, Zhenggang Wang, Ming Liu, Jing Zou, Zhipeng Zhao, Jingchao Yan and Junyu Guo
Processes 2024, 12(10), 2094; https://doi.org/10.3390/pr12102094 - 26 Sep 2024
Viewed by 920
Abstract
This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two [...] Read more.
This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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18 pages, 3667 KiB  
Article
An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
by Hubin Du, Qiuyu Li, Ziqian Guan, Hengyuan Zhang and Yongtao Liu
Processes 2024, 12(9), 1978; https://doi.org/10.3390/pr12091978 - 13 Sep 2024
Viewed by 748
Abstract
The efficacy of early fire detection hinges on its swift response and precision, which allows for the issuance of timely alerts in the nascent stages of a fire, thereby minimizing losses and injuries. To enhance the precision and swiftness of identifying minute early [...] Read more.
The efficacy of early fire detection hinges on its swift response and precision, which allows for the issuance of timely alerts in the nascent stages of a fire, thereby minimizing losses and injuries. To enhance the precision and swiftness of identifying minute early flame targets, as well as the ease of deployment at the edge end, an optimized early flame target detection algorithm for YOLOv8 is proposed. The original feature fusion module, an FPN (feature pyramid network) of YOLOv8n, has been enhanced to become the BiFPN (bidirectional feature pyramid network) module. This modification enables the network to more efficiently and rapidly perform multi-scale fusion, thereby enhancing its capacity for integrating features across different scales. Secondly, the efficient multi-scale attention (EMA) mechanism is introduced to ensure the effective retention of information on each channel and reduce the computational overhead, thereby improving the model’s detection accuracy while reducing the number of model parameters. Subsequently, the NWD (normalized Wasserstein distance) loss function is employed as the bounding box loss function, which enhances the model’s regression performance and robustness. The experimental results demonstrate that the size of the enhanced model is 4.8 M, a reduction of 22.5% compared to the original YOLOv8n. Additionally, the mAP0.5 metric exhibits a 2.7% improvement over the original YOLOv8n, indicating a more robust detection capability and a more compact model size. This makes it an ideal candidate for deployment in edge devices. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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18 pages, 1740 KiB  
Article
Deep Integration and Innovation Development in the Logistics and Manufacturing Industries and Their Performances: A Case Study of Anhui Province, China
by Heping Ding, Yuchang Gao, Fagang Hu, Yuxia Guo and Conghu Liu
Processes 2024, 12(9), 1867; https://doi.org/10.3390/pr12091867 - 31 Aug 2024
Viewed by 724
Abstract
The deep integration and innovative development of the logistics and manufacturing industries (LMDIIs) are crucial for reducing costs, increasing efficiency, and advancing manufacturing. To assess the development level and performance of the LMDIIs, we construct an evaluation index system, calculate the weights using [...] Read more.
The deep integration and innovative development of the logistics and manufacturing industries (LMDIIs) are crucial for reducing costs, increasing efficiency, and advancing manufacturing. To assess the development level and performance of the LMDIIs, we construct an evaluation index system, calculate the weights using the CRITIC method, and measure the comprehensive level of the LMDIIs using the TOPSIS method. We evaluate the coupling coordination of the LMDIIs and conduct a ridge regression analysis of their performance, using Anhui Province, China, as a case study. The results show that the comprehensive level of the LMDIIs in Anhui Province is low. The highest values for the logistics and manufacturing industries from 2013 to 2022 indicate great development potential. The coupling level is fluctuating upwards, and the coupling degree is growing slowly. The performance impact coefficients of the LMDIIs on the digital intelligence development of the manufacturing industry and the profit levels of the two industries indicate a significant promoting effect. However, the performance coefficient for the low-carbon transformation of the logistics industry is negative, indicating a restraining effect. Hence, we propose countermeasures and suggestions to further promote the LMDIIs and provide theoretical and methodological support for their research and management. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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14 pages, 3261 KiB  
Article
A Lightweight Safety Helmet Detection Algorithm Based on Receptive Field Enhancement
by Changpeng Ji, Zhibo Hou and Wei Dai
Processes 2024, 12(6), 1136; https://doi.org/10.3390/pr12061136 - 31 May 2024
Viewed by 648
Abstract
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet [...] Read more.
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet detection algorithm called YOLOv5s-CR. First, we use a lightweight backbone, a high-resolution feature fusion network, and a small object detection layer to improve the detection accuracy of small objects while substantially decreasing the model parameters. Next, we embed a coordinate attention mechanism into the feature extraction network to improve the localization accuracy of the detected object. Finally, we propose a new receptive field enhancement module (RFEM) to substitute the SPPF module in the original network, enabling the model to acquire features under multiple receptive fields, thereby enhancing the detection precision of multi-scale objects. Using the Safety Helmet Detection dataset for validation, in contrast to the initial YOLOv5s, the parameters of the improved algorithm were reduced by 62.8% to 2.61 M, and P, R, and mAP0.5 were increased by 1.5%, 1.2%, and 2.0%, respectively. The detection speed can reach 149FPS on the RTX3070 GPU, which satisfies the accuracy and real-time requirements for detecting safety helmets. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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