  Data-driven industrial health prognostics require rich training data to
develop accurate and reliable predictive models. However, stringent data
privacy laws and the abundance of edge industrial data necessitate
decentralized data utilization. Thus, the industrial health prognostics field
is well suited to significantly benefit from federated learning (FL), a
decentralized and privacy-preserving learning technique. However, FL-based
health prognostics tasks have hardly been investigated due to the complexities
of meaningfully aggregating model parameters trained from heterogeneous data to
form a high performing federated model. Specifically, data heterogeneity among
edge devices, stemming from dissimilar degradation mechanisms and unequal
dataset sizes, poses a critical statistical challenge for developing accurate
federated models. We propose a pioneering FL-based health prognostic model with
a feature similarity-matched parameter aggregation algorithm to
discriminatingly learn from heterogeneous edge data. The algorithm searches
across the heterogeneous locally trained models and matches neurons with
probabilistically similar feature extraction functions first, before
selectively averaging them to form the federated model parameters. As the
algorithm only averages similar neurons, as opposed to conventional naive
averaging of coordinate-wise neurons, the distinct feature extractors of local
models are carried over with less dilution to the resultant federated model.
Using both cyclic degradation data of Li-ion batteries and non-cyclic data of
turbofan engines, we demonstrate that the proposed method yields accuracy
improvements as high as 44.5\% and 39.3\% for state-of-health estimation and
remaining useful life estimation, respectively.
