Global Composite Compression of Deep Neural Network in Wireless Sensor Networks for Edge Intelligent Fault Diagnosis

Huan, Liaoyuan; Tang, Baoping; Zhao, Chunhua

doi:10.1109/jsen.2023.3290153

IEEE Sensors J.

2023

DOI: 10.1109/jsen.2023.3290153

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Global Composite Compression of Deep Neural Network in Wireless Sensor Networks for Edge Intelligent Fault Diagnosis

Liaoyuan Huan,

Baoping Tang,

Chunhua Zhao

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2024

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Cited by 3 publications

References 29 publications

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Global probability distribution structure-sparsity filter pruning for edge fault diagnosis in resource constrained wireless sensor networks

Zhao,

Tang,

Deng

et al. 2024

Engineering Applications of Artificial Intelligence

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Global probability distribution structure-sparsity filter pruning for edge fault diagnosis in resource constrained wireless sensor networks

Zhao,

Tang,

Deng

et al. 2024

Engineering Applications of Artificial Intelligence

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MKDC: A Lightweight Method for Cloud-Edge Collaborative Fault Diagnosis Model

Wang,

Zhang,

Yang

et al. 2024

IEEE Sensors J.

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Edge Computing-Based Digital Twin Framework Based on ISO 23247 for Enhancing Data Processing Capabilities

Kang,

Lee,

Bajestani

et al. 2024

Machines

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Digital Twin (DT) technology has evolved to contextualize real-time interactions between the physical and digital worlds. It has gained increasing attention in various research areas and industries, including the manufacturing, aerospace, and automotive industries, while facing challenges in processing and analyzing large datasets. To address these challenges, this paper proposes an edge computing-based DT (E-DT) framework and elaborates on its functional and informational aspects. The functional aspect is represented using ISO 23247 as the reference architecture, while the informational aspect is elaborated by introducing a data fusion model. Then, an E-DT for Wire+Arc Additive Manufacturing (WAAM) is developed, its performance is evaluated, and the benefits of edge computing in enhancing real-time processing and decision-making are verified. In addition, a typical non-edge computing-based DT for WAAM is implemented according to ISO 23237 for comparison. The results show that the proposed E-DT for WAAM achieved reduction in latency through faster data processing, smaller data upload volume, and more consistent data upload speed.

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Global Composite Compression of Deep Neural Network in Wireless Sensor Networks for Edge Intelligent Fault Diagnosis

Cited by 3 publications

References 29 publications

Global probability distribution structure-sparsity filter pruning for edge fault diagnosis in resource constrained wireless sensor networks

Global probability distribution structure-sparsity filter pruning for edge fault diagnosis in resource constrained wireless sensor networks

MKDC: A Lightweight Method for Cloud-Edge Collaborative Fault Diagnosis Model

Edge Computing-Based Digital Twin Framework Based on ISO 23247 for Enhancing Data Processing Capabilities

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