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指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确保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重大成功，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。虽然集中式数据收集可以提高检测准确性，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，从而形成了工业数据孤岛的问题。为了应对这些挑战，本文提出了一种类原型指导的个性化轻量级联邦学习框架 （FedCPG）。该框架解耦了本地网络，只将主干模型上传到服务器进行模型聚合，同时采用头部模型进行本地个性化更新，从而实现高效的模型聚合。此外，该框架还结合了原型约束来指导本地个性化更新过程，从而减轻数据异质性的影响。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 在复杂工业场景下可以达到 95% 的平均检测准确率，同时将内存使用和参数数量减少 82%，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的视角。