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Fault Diagnosis Platform Based on the IoT and Intelligent Computing
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Fault Diagnosis Platform Based on the IoT and Intelligent Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 2544

Special Issue Editor

Prof. Dr. Robert Hsu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
Interests: cloud computing; IoT; RFID; big data; edge & fog computing; distributed systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of various smart digital sensors, the Internet of Thing (IoT) technology is evolving rapidly and contributing substantially to Industry 4.0 and the promotion of monitoring systems. The use of intelligent IoT sensors drives smart manufacturing and raises new requirements and challenges for fault diagnosis platforms in terms of efficiency, reliability, and data security. In the era of big data and IoT, the integration of cloud–edge computing technologies, cyberphysical systems, and artificial intelligence provides huge potential for accurate fault diagnosis and predictive maintenance in power transformer cyberattacks, battery lifetime predictions, rotating machine faults, etc.

This Special Issue seeks innovative works on a wide range of research topics, which include (but are not limited to) the following:

  1. Industrial system security;
  2. Advanced cloud-assisted intelligent architecture in smart factories;
  3. End–edge–cloud-orchestrated fault diagnosis platforms;
  4. Integrated framework employing IoT and big data techniques;
  5. Artificial Intelligence of Things systems;
  6. Fault diagnosis methods based on big data analysis;
  7. Deep learning applications in industrial security challenges.

We would like to invite you to submit an article to this Special Issue, including short communications, full research articles, and timely reviews.

Prof. Dr. Robert Hsu
Guest Editor

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • fault diagnosis
  • predictive maintenance
  • Industry 4.0
  • Internet of Things
  • cloud–edge computing
  • cyberphysical system
  • artificial intelligence

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

Published Papers (2 papers)

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Research

22 pages, 2115 KiB  
Article
DyGAT-FTNet: A Dynamic Graph Attention Network for Multi-Sensor Fault Diagnosis and Time–Frequency Data Fusion
by Hongjun Duan, Guorong Chen, Yuan Yu, Chonglin Du, Zhang Bao and Denglong Ma
Sensors 2025, 25(3), 810; https://doi.org/10.3390/s25030810 - 29 Jan 2025
Cited by 1 | Viewed by 819
Abstract
Fault diagnosis in modern industrial and information systems is critical for ensuring equipment reliability and operational safety, but traditional methods have difficulty in effectively capturing spatiotemporal dependencies and fault-sensitive features in multi-sensor data, especially rarely considering dynamic features between multi-sensor data. To address [...] Read more.
Fault diagnosis in modern industrial and information systems is critical for ensuring equipment reliability and operational safety, but traditional methods have difficulty in effectively capturing spatiotemporal dependencies and fault-sensitive features in multi-sensor data, especially rarely considering dynamic features between multi-sensor data. To address these challenges, this study proposes DyGAT-FTNet, a novel graph neural network model tailored to multi-sensor fault detection. The model dynamically constructs association graphs through a learnable dynamic graph construction mechanism, enabling automatic adjacency matrix generation based on time–frequency features derived from the short-time Fourier transform (STFT). Additionally, the dynamic graph attention network (DyGAT) enhances the extraction of spatiotemporal dependencies by dynamically assigning node weights. The time–frequency graph pooling layer further aggregates time–frequency information and optimizes feature representation.Experimental evaluations on two benchmark multi-sensor fault detection datasets, the XJTUSuprgear dataset and SEU dataset, show that DyGAT-FTNet significantly outperformed existing methods in classification accuracy, with accuracies of 1.0000 and 0.9995, respectively, highlighting its potential for practical applications. Full article
(This article belongs to the Special Issue Fault Diagnosis Platform Based on the IoT and Intelligent Computing)
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18 pages, 4835 KiB  
Article
Research on Bearing Surface Scratch Detection Based on Improved YOLOV5
by Huakun Jia, Huimin Zhou, Zhehao Chen, Rongke Gao, Yang Lu and Liandong Yu
Sensors 2024, 24(10), 3002; https://doi.org/10.3390/s24103002 - 9 May 2024
Cited by 2 | Viewed by 1286
Abstract
Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection [...] Read more.
Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings. Full article
(This article belongs to the Special Issue Fault Diagnosis Platform Based on the IoT and Intelligent Computing)
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