Balance recovery and collaborative adaptation approach for federated fault diagnosis of inconsistent machine groups

Due to data privacy concerns and long-distance communication overhead, federated learning-based intelligent diagnosis offers a promising solution for ensuring the efficiency and reliability of machine groups in data decentralization. However, the data information from different machine nodes in a group are often inconsistent, leading to two key challenges in current federated intelligent diagnosis research: (1) data imbalance especially with respect to unseen faults, which causes the diagnosis model to become skewed, and (2) label space shifts across machine nodes, resulting in significant misalignment between the local and global distributions. As a consequence, the global diagnosis model struggles to effectively recognize unseen and under-represented fault states, and is often under-generalized to other machine nodes, especially when only a limited number of labeled samples are available. To address these challenges, this article presents a balance recovery and collaborative adaptation (BRCA) framework for federated intelligent diagnosis. The BRCA framework utilizes a central server to capture the inconsistent distribution information from each machine node, and further solves the Wasserstein barycenter to create a global distribution that carries complementary information. This barycenter is then broadcast to the client side to guide local model updates. At each client, convolutional autoencoders are constrained to supplement synthetic data for unseen and under-represented fault states, helping to restore a balanced decision boundary. Moreover, local distributions are aligned with the global barycenter through the designed adaptation trajectory that directionally ties subcategories with the same label. This is expected to correct discrepancies caused by label space shifts. The proposed BRCA is demonstrated in two federated intelligent diagnosis cases: one involving diverse machine-used bearings and the other involving different planetary gearboxes. The results show that BRCA can mitigate the performance degradation caused by data inconsistency, and achieve higher diagnosis accuracy than existing federated methods on other machine nodes even when there are very few labeled samples available.