BIG DATA
PRECISION SCHEDULED MAINTENANCE
How CSX uses advanced analytics and autonomous data collection systems in m/w tasks. inear asset management is a high-tech term that defines what goes into railroad maintenance-of-way. It’s a rapidly evolving area that increasingly relies on autonomous or semi-autonomous data collection systems, coupled with Big Data-driven analytics (p. 39). Among the many railroad/supplier collaborative efforts is one involving CSX and Greenville, S.C.-based VisioStack, a developer of condition monitoring, data analytics and decision support systems. The company’s rail-centric offerings are RAILLINKS® AI and RAILLINKS® PREDICT. As CSX’s Director of Track Testing, Brad Spencer has a lot under his umbrella. “There are many ways to conduct testing,” he says. “We use autonomous boxcars, our ATAC (Autonomous Track Assessment Cars) system, because they’re a bit more modular, and we’re not using a resource that’s quite as restrictive as a locomotive, because a lot of
46 Railway Age // March 2021
these platforms are still under development. We have not used machine vision until this year, but we conduct frequent testing, which is critical for doing predictive analytics and deep learning. The foundation of autonomous systems is data management, where we can synchronize all our different data platforms. There’s no better way than visualization to get information out in the field. We use various system providers and technologies. If there’s no way to marry and sync all that data together, it doesn’t matter how many times you collect it.” “We started working in November 2018 with VisioStack,” explains Spencer. “We began with a small project, looking at curve alignment issues on one specific route, and we were in discussions with several companies about data management. We liked VisioStack’s platform, and wanted to see what they could do with it. We were pretty impressed with that initial project. It only took a few weeks, but it gave us some idea of what to look
forward to and what their potential is.” “We’ve been working on this technology for about 10 years, when we started seeing a trend toward autonomous data collection systems,” says VisioStack President and CEO Zachary G. Garner. “This meant there was going to be a lot more inundation of data. We set out to build a Cloud-based platform that can handle any type of railway asset or condition data, meaning track geometry, rail wear or linear sample data, as we call it, point cloud data—whether that’s LIDAR, or rail profile, or any type of imagery data as well as defects. Defects come from many different systems. “We’ve really been working on workflows, which means whatever data we get, we try to establish an automated process by which decisions are made. So it’s very critical, if we’re going toward autonomous data collection systems, that we have business rules and automated processes in place that allow us to validate data quality and to perform various flexible tasks on the data, and then ultimately railwayage.com
VisioStack (all images)
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BY WILLIAM C. VANTUONO, EDITOR-IN-CHIEF
