Huiqi Zhao
ABSTRACT
The Industrial Internet of Things (IIoT) represents the deployment of Internet of Things (IoT) technology in industrial applications. In this article, we address the challenges of fault diagnosis and data privacy protection within the IIoT environment. We present a novel fault diagnosis model that combines federated learning and a particle swarm optimization algorithm. Firstly, we introduce a three-tier federated learning model designed to safeguard the data privacy of each participant in a real industrial control network structure. Subsequently, we enhance the particle swarm optimization algorithm to augment its global exploration capabilities and convergence performance, enabling it to collect federated learning model weights in lieu of traditional techniques. Furthermore, we employ the Taguchi method to tailor an optimal solution for the modified particle swarm optimization algorithm (TMPSO), thereby enhancing the algorithm's efficiency and robustness. Additionally, we propose a neural network model utilizing small convolutional kernels (SVGG) for fault diagnosis within the IIoT framework, thereby improving the model's feature learning capabilities. Experimental validation was conducted using actual industrial rolling bearing datasets and CIFAR-10 datasets. The results of these experiments demonstrate that our proposed TPMPSO-SVGG model outperforms other methods in terms of fault identification accuracy and communication cost.
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Index Terms
- A Federated Learning Model for Fault Diagnosis of IIoT Using a Modified PSO Algorithm Customized by Taguchi Method
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