Industrial equipment condition monitoring and fault detection are crucial to ensure the reliability of industrial production. Recently, data-driven fault detection methods have achieved significant success, but they all face challenges due to data fragmentation and limited fault detection capabilities. Although centralized data collection can improve detection accuracy, the conflicting interests brought by data privacy issues make data sharing between different devices impractical, thus forming the problem of industrial data silos. To address these challenges, this paper proposes a class prototype guided personalized lightweight federated learning framework(FedCPG). This framework decouples the local network, only uploading the backbone model to the server for model aggregation, while employing the head model for local personalized updates, thereby achieving efficient model aggregation. Furthermore, the framework incorporates prototype constraints to steer the local personalized update process, mitigating the effects of data heterogeneity. Finally, a lightweight feature extraction network is designed to reduce communication overhead. Multiple complex industrial data distribution scenarios were simulated on two benchmark industrial datasets. Extensive experiments have demonstrated that FedCPG can achieve an average detection accuracy of 95% in complex industrial scenarios, while simultaneously reducing memory usage and the number of parameters by 82%, surpassing existing methods in most average metrics. These findings offer novel perspectives on the application of personalized federated learning in industrial fault detection.

中文翻译：

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FedCPG：用于跨工厂故障检测的类原型引导的个性化轻量级联邦学习框架


工业设备状态监测和故障检测对于保证工业生产的可靠性至关重要。近年来，数据驱动的故障检测方法取得了巨大的成功，但由于数据碎片化和故障检测能力有限，它们都面临着挑战。虽然集中式数据采集可以提高检测精度，但数据隐私问题带来的利益冲突使得不同设备之间的数据共享变得不切实际，从而形成工业数据孤岛问题。为了应对这些挑战，本文提出了一种类原型引导的个性化轻量级联邦学习框架（FedCPG）。该框架解耦本地网络，仅将骨干模型上传到服务器进行模型聚合，而采用头模型进行本地个性化更新，从而实现高效的模型聚合。此外，该框架结合了原型约束来引导本地个性化更新过程，减轻数据异构性的影响。最后，设计了一个轻量级的特征提取网络来减少通信开销。在两个基准工业数据集上模拟了多个复杂的工业数据分布场景。大量实验表明，FedCPG 在复杂的工业场景中可以实现 95% 的平均检测精度，同时减少 82% 的内存占用和参数数量，在大多数平均指标上超越现有方法。这些发现为个性化联邦学习在工业故障检测中的应用提供了新颖的视角。