AHSI: Big Data for better asset health

Written by William Vantuono, editor-in-chief, Railway Age
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The railroad industry estimates that advances in fault detection technology are preventing more than 700 road failures monthly on the North American system, based on 2019 preliminary statistics. Among the efforts to identify the causes of failure and further understand issues that affect freight rail performance and safety is the Association of American Railroads Asset Health Strategic Initiative (AHSI), data from which is shared with Railinc.

AHSI is a multi-year, multi-phase railroad industry program that applies information technology solutions and processes to address asset health problems with wheels, bearings, axles, brake systems and other railcar components. Individual railroads accumulate information over time to detect potential component defects. The health data is shared with Railinc, which then produces a comprehensive, equipment-level analysis of asset health.

The program, said Railinc, “seeks to reduce mechanical service interruptions, improve the quality of railcar inspections, and increase rail yard and repair shop efficiency.” AHSI takes into account “the entire rolling stock health cycle, which incorporates prevention, detection, planning, movement, repair and settlement.”

“Using new algorithms, we are able to more quickly identify railcars that have repeated mechanical failures,” said Railinc Director of Asset Services Chip Summey. “Once identified, railroads can take necessary steps to repair and correct the causes.”

“Railroad industry investments over the past decade have made an impact on asset health issues, but a broader strategic focus at the network level will provide significant returns and greater efficiencies,” Railinc notes. “AHSI builds on existing local investments in detectors and systems and existing industry capabilities such as Umler® (Equipment Registry), CEPM (Component Registry), CRB (Car Repair Billing), EHMS/InteRRIS (Detector Alerts), and DDCT (Damaged and Defective Car Tracking) to build a bridge to information collaboration. One recent example of this is CEPM, which enables component-performance management to occur in a more comprehensive way than a single railroad or car owner could achieve.”

Summey said data flowing from wayside detection networks and train events help single-out the cause of road failures. Those detection networks “are getting faster and smarter. Enabling issues to be more quickly identified reduces future road failures, improves safety and saves money. The railroad industry made a significant investment in upgrading these networks, reducing equipment and track damage, as well as preventing failures and optimizing maintenance.”

AHSI will consider the entire rolling stock health cycle, which incorporates prevention, detection, planning, movement, repair and settlement.

In 2019, AHSI continued efforts to share updated data regarding railcar repairs, and also increase data collection and analysis of railcar component issues over time. “The benefits of increased asset health management are significant because all the players are working together,” said Summey. “The improved safety and the annual savings are an important victory for railroads and their customers.”

AHSI’s foundational work, which began in 2013, included:

  • Asset Information Repository, a comprehensive equipment level view of asset health and characteristic data.
  • E-Train, a centralized database of train information that enables real-time visibility and analysis of consist data to reduce manual work, improve efficiency and enable better decision making around maintenance and repair tasks.
  • Inspection Quality (Detector Repository), a comprehensive database of detector reads to enable more-effective health monitoring of equipment and improve repair work efficiency.
  • Mechanical Reference Repository, a common repository for current and historical operational reference data and an automated means for their use.

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Categories: Class 1, Freight, Railroad News, Safety/Training, Shortline/Regional
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