Abstract
Deep learning (DL) approaches have multiple potential advantages that have been explored in various fields, but for prognostic and health management (PHM) applications, this is not the case due to the lack of data in particular applications and also due of the absence of multiple DL-oriented benchmarks as in other fields, which limits the research in this area even though these types of applications will have a strong impact on the industrial world. To introduce the benefits of DL in this area, we should be able to develop models even when we have small data sets, to verify whether or not this is possible, in this thesis we explore the research direction of few shot learning in the context of equipment PHM.
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