This paper aims to develop a statistical method for integrating multiple incomplete data sources to improve the estimation of the failure-rate probability distribution of fusion-specific devices. The data aggregation approach proposed in this work is to support the Reliability, Availability, Maintainability, and Inspectability (RAMI) studies for fusion-oriented research facilities. This work focuses on developing a procedure for handling missing or incomplete data from multiple sources, as well as formulating a statistical framework for combining data from different sources that refer to the same equipment type and failure mode. Specifically, a Bayesian result is adopted under the assumption of a constant failure rate over time. This framework motivates the use of the Gamma distributions to model the failure-rate parameter of each device. Subsequently, two alternative procedures are introduced to approximate, using a single Gamma distribution, the weighted mixture formed by combining Gamma distributions corresponding to the same equipment type across multiple data sources. The proposed method is validated through an extensive simulation study and further illustrated by application to the accelerator systems of the IFMIF-DONES facility. Finally, the proposed algorithm has been implemented in the open-source software R to facilitate its use in the nuclear fusion community.

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