论文标题
预后和健康管理中的计算可重复性
Computational Reproducibility Within Prognostics and Health Management
论文作者
论文摘要
科学研究经常涉及使用计算工具和方法。提供彻底的文档，开源代码和数据（创建可再现的计算研究），可帮助他人了解研究人员的工作。在这里，我们广泛地探讨了预后和健康管理（PHM）领域的计算可重复性。在科学学科和PHM中，可再现的计算研究实践的采用仍然很低。我们从从事PHM研究的出版物的300多种文章的文本挖掘表明，不到1％的研究人员将其代码和数据提供给其他人。尽管仍然存在挑战，但也有明显的机会和利益，可以进行可再现的计算研究。我们介绍了一个机会，我们引入了一种名为Pyphm的开源软件工具，以帮助PHM研究人员访问和预处理常见的工业数据集。
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 -- helps others understand a researcher's work. Here, we explore computational reproducibility, broadly, and from within the field of prognostics and health management (PHM). The adoption of reproducible computational research practices remains low across scientific disciplines and within PHM. Our text mining of more than 300 articles, from publications engaged in PHM research, showed that fewer than 1% of researchers made their code and data available to others. Although challenges remain, there are also clear opportunities, and benefits, for engaging in reproducible computational research. Highlighting an opportunity, we introduce an open-source software tool, called PyPHM, to assist PHM researchers in accessing and preprocessing common industrial datasets.
