Leveraging Big Data to Discover High Yield Prognostics Applications

A common scenario for many organizations in this information age entails volumes of data that are easily collected and stored in repositories, but are not subsequently leveraged to improve products or processes due to a lack of resources, such as knowledgeable personnel or sufficient computing power. Rather than being collected as the result of a specific mandate for improvement, the data are collected simply because it is easy and inexpensive to do so, and because the data allegedly possess some, as of yet unknown, value. As the field of Prognostics and Health Management (PHM) grows, the research that is invested into finding the remaining useful life (RUL) of high value components can leverage these data repositories in many different ways. However, the task can seem overwhelming when there is no clear direction on how to use data to further specific goals. In this paper, we discuss the importance of PHM and provide a data-driven approach for discovering impactful uses of data for PHM challenges. This approach is illustrated through a research effort that explored a set of vehicle maintenance data and determined that analysis of vehicle generators would constitute the best initial return on investment.