Model selection is a crucial aspect of Prognostics and Health Management (PHM). However, many PHM models are de- veloped for specific datasets and lack flexibility to adapt to different datasets with varying data quality considerations. To address this gap, we propose a generalizable model selection pipeline for PHM. Our approach involves creating a pipeline for testing models that users can tune in various ways. We designed a sequential pipeline of steps for model selection with a focus on implementation considerations which include recommendations for handling environmental variables, ca- pabilities for remote and local work environments, and stor- age considerations of the serialized pipeline. Performance metrics are designed to consider data quality characteristics such as ambiguous labeling. We illustrate the generalizability of our approach through a case study of our model selection pipeline applied to a field dataset with ambiguous labels. Our design accommodates data characteristics commonly found in field data, such as ambiguous labels and data wrangling. Our contribution fills a gap in real-world implementations of PHM by offering technology considerations and recommen- dations for effective deployment.
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Fault Detection, Modeling Pipeline, Data/Model Operations
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