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Machines
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Recent Trends in Sensor Fusion Algorithms Using Intelligent Signal Processing...
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Recent Trends in Sensor Fusion Algorithms Using Intelligent Signal Processing Methods

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 5253

Special Issue Editors

Dr. Hongtian Chen

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Interests: fault detection and diagnosis; high-speed trains; data mining and analytics; machine learning; quantum computation
Special Issues, Collections and Topics in MDPI journals
Dr. Yiyang Chen

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215031, China
Interests: intelligent control; artificial intelligence; image processing; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Engang Tian

E-Mail Website
Guest Editor
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
Interests: etworked control systems; network attack and security control; probabilistic constraint control; wireless charging of electric vehicles; analysis and synthesis of fuzzy control systems
Prof. Dr. Hui Yu

E-Mail Website
Guest Editor
Faculty of Creative and Cultural Industries, School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UK
Interests: ocean intelligent computing; digital vision technology; human-machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing popularity of artificial intelligence (AI) has led to its application in various fields. With the widespread use of AI, sensor-fusion-powered signal processing methods have become extremely important. AI serves as the soft power source for cybernetic systems to perform various delicate tasks. The movement of robots is measured by multiple sensors, and the sensors provide data for subsequent motion to participate in a decision-making process based on data analysis, which forms a complete closed loop. Nowadays, the number of devices connected to the Internet exceeds the world’s population. These devices are equipped with various types of sensors, which has led to an explosion of data. Such a massive amount of data cannot be completely analyzed and processed by humans; AI intervention is required to improve the efficiency of sensor fusion technology, which is regarded as intelligent data analysis. AI can be applied for data processing and pattern recognition, and it allows computers to learn without programming and process large amounts of data in a short period of time. This allows researchers to focus on certain tasks in greater depth. Potential topics for this Special Issue include, but are not limited to, the following:

  • AI-powered sensor signal processing;
  • Intelligent analysis and diagnosis methods;
  • Optimization of intelligent control using sensor fusion;
  • Explainable fault diagnosis methods for sensors;
  • Computer vision-based sensing;
  • Coordinated control of multiple sensors;
  • Stability analysis of sensors using AI.

Dr. Hongtian Chen
Dr. Yiyang Chen
Prof. Dr. Engang Tian
Prof. Dr. Hui Yu
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. Machines 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.

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

Published Papers (3 papers)

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Research

23 pages, 3917 KiB  
Article
MRP-YOLO: An Improved YOLOv8 Algorithm for Steel Surface Defects
by Shuxian Zhu and Yajie Zhou
Machines 2024, 12(12), 917; https://doi.org/10.3390/machines12120917 - 14 Dec 2024
Cited by 1 | Viewed by 1523
Abstract
The existing detection algorithms are unable to achieve a suitable balance between detection accuracy and inference speed. As the accuracy of the algorithm increases, its complexity also rises, resulting in a decrease in detection speed, which undermines its practicality. This issue is particularly [...] Read more.
The existing detection algorithms are unable to achieve a suitable balance between detection accuracy and inference speed. As the accuracy of the algorithm increases, its complexity also rises, resulting in a decrease in detection speed, which undermines its practicality. This issue is particularly evident in the context of surface defect detection in industrial parts, where low contrast, small target features, difficult feature extraction, and low real-time detection efficiency are prominent challenges. This study proposes a novel method for steel defect detection based on the YOLO v8 algorithm, which improves detection accuracy while maintaining low computational complexity. Firstly, the global background and edge information are adaptively extracted via the MSA-SPPF module in order to obtain a more comprehensive feature representation. Furthermore, the anti-interference ability of the model is enhanced through the deformability of attention and the large convolution kernel characteristics. Secondly, the design of Dynamic Conv and C2f-OREPA enables the model to efficiently reduce the demand for computational resources while maintaining high performance. It is further proposed that the RepHead detection head approximates the multi-branch structure of the original training by a single convolution operation. This approach not only enriches the feature representation but also maintains an efficient inference process. The effectiveness of the improved MRP-YOLO algorithm is verified using the NEU-DET industrial surface defect dataset. The experimental results demonstrate that the mAP of the MRP-YOLO algorithm reaches 75.6%, which is 2.2% higher than that of the YOLOv8n algorithm, while the FLOPs are only 2.3 G higher. It indicates that the detection accuracy is significantly improved with a limited increase in computational complexity. Full article
(This article belongs to the Special Issue Recent Trends in Sensor Fusion Algorithms Using Intelligent Signal Processing Methods)
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15 pages, 3195 KiB  
Article
Improved Bayes-Based Reliability Prediction of Small-Sample Hall Current Sensors
by Ting Chen, Zhengyu Liu, Ling Ju, Yongling Lu and Shike Wei
Machines 2024, 12(9), 618; https://doi.org/10.3390/machines12090618 - 4 Sep 2024
Viewed by 927
Abstract
As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. [...] Read more.
As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model. Full article
(This article belongs to the Special Issue Recent Trends in Sensor Fusion Algorithms Using Intelligent Signal Processing Methods)
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13 pages, 3043 KiB  
Article
Predicting Assembly Geometric Errors Based on Transformer Neural Networks
by Wu Wang, Hua Li, Pei Liu, Botong Niu, Jing Sun and Boge Wen
Machines 2024, 12(3), 161; https://doi.org/10.3390/machines12030161 - 27 Feb 2024
Viewed by 1499
Abstract
Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known [...] Read more.
Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models. Full article
(This article belongs to the Special Issue Recent Trends in Sensor Fusion Algorithms Using Intelligent Signal Processing Methods)
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