  Deep learning-based fault diagnosis (FD) approaches require a large amount of
training data, which are difficult to obtain since they are located across
different entities. Federated learning (FL) enables multiple clients to
collaboratively train a shared model with data privacy guaranteed. However, the
domain discrepancy and data scarcity problems among clients deteriorate the
performance of the global FL model. To tackle these issues, we propose a novel
framework called representation encoding-based federated meta-learning (REFML)
for few-shot FD. First, a novel training strategy based on representation
encoding and meta-learning is developed. It harnesses the inherent
heterogeneity among training clients, effectively transforming it into an
advantage for out-of-distribution generalization on unseen working conditions
or equipment types. Additionally, an adaptive interpolation method that
calculates the optimal combination of local and global models as the
initialization of local training is proposed. This helps to further utilize
local information to mitigate the negative effects of domain discrepancy. As a
result, high diagnostic accuracy can be achieved on unseen working conditions
or equipment types with limited training data. Compared with the
state-of-the-art methods, such as FedProx, the proposed REFML framework
achieves an increase in accuracy by 2.17%-6.50% when tested on unseen working
conditions of the same equipment type and 13.44%-18.33% when tested on totally
unseen equipment types, respectively.
