Computational Reproducibility Within Prognostics and Health Management

Tim von Hahn; Chris K. Mechefske

doi:10.37965.jdmd.2023.141

Authors

Tim von Hahn Department of Mechanical and Materials Engineering, Queen’s University, Canada
Chris K. Mechefske Department of Mechanical and Materials Engineering, Queen’s University, Canada https://orcid.org/0000-0002-3509-0339

DOI:

https://doi.org/10.37965.jdmd.2023.141

Keywords:

computational reproducibility, open-source, prognostics and health management

Abstract

Scientific research frequently involves the use of computational tools and methods. Providing thorough documentation, open-source code, and data – the creation of reproducible computational research (RCR) – helps others understand a researcher’s work. In this study, we investigate the state of reproducible computational research, broadly, and from within the field of prognostics and health management (PHM). In a text mining survey of more than 300 articles, we show that fewer than 1% of PHM researchers make their code and data available to others. To promote the RCR further, our work also highlights several personal benefits for those engaged in the practice. Finally, we introduce an open-source software tool, called PyPHM, to assist PHM researchers in accessing and preprocessing common industrial datasets.

Conflict of Interest Statement

The authors declare no conflicts of interest.