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.
翻译:科学研究经常涉及使用计算工具和方法。提供详尽的文件、开放源代码和数据 -- -- 创建可复制的计算研究 -- -- 帮助他人理解研究人员的工作。在这里,我们广泛探讨计算再复制问题,并从预测和健康管理(PHM)领域探索。采用可复制的计算研究方法在科学学科和PHM内部仍然很少。我们从从事PHM研究的出版物中挖掘300多篇文章的文本,显示不到1%的研究人员向他人提供了他们的代码和数据。尽管挑战依然存在,但参与可复制计算研究也存在明确的机会和好处。我们强调一个机会,即引入一个名为PyPHM的开放源软件工具,以协助PHM研究人员访问和预处理共同的工业数据集。