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Fault Diagnosis Platform Based on the IoT and Intelligent Computing

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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:

Industrial system security;
Advanced cloud-assisted intelligent architecture in smart factories;
End–edge–cloud-orchestrated fault diagnosis platforms;
Integrated framework employing IoT and big data techniques;
Artificial Intelligence of Things systems;
Fault diagnosis methods based on big data analysis;
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

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22 pages, 2115 KiB

Open AccessArticle

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 924

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 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

Open AccessArticle

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 1328

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 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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