Multifunctional health status assessment based on decentralized federated temporal domain adaptation for rotating machinery

Based on the training with the large amounts of labeled data, intelligent data-driven approaches are able to assess the health status of rotating machinery. Traditional methods usually lack the multifunctionality required for health status assessment (HSA), resulting in incomplete assessment results. Furthermore, considering the challenge of machinery data silos, existing methods use collaborative model training solutions with multiple users, which place high demands on data privacy protection due to conflict of interests. To tackle this issue, a multifunctional HSA method based on decentralized federated temporal domain adaptation is proposed in this paper. First, a novel multifunctional HSA framework is designed for comprehensive assessment of rotating machinery, which performs synchronous health stage division and recognition, remaining useful life prediction, and reliability evaluation. Then, a temporal adversarial domain adaptation model is proposed to align temporal feature distributions through adversarial training between cross-domain encoders and a discriminator, both integrated with channel attention to enhance temporal feature extraction capability. Meanwhile, data privacy is ensured through decentralized federated learning involving multiple users. Experimental results show that the proposed method exhibits better generalization and stability, achieving an average error of 0.087 in dual prediction functions and an accuracy of 0.94 in recognition functionality.