Saved USD 1 M by implementing Wheel Health Monitoring System for a leading North American Railroad Operator

Case Study

Saved USD 1 M by implementing Wheel Health Monitoring System for a leading North American Railroad Operator

Business Objective

Our client is a leading operator in the North American railroad industry with a fleet of 200,000+ railcars. The client had WILD (Wheel Impact Load Detector) System – a wayside sensor that detects faulty wheels. Flooding, weather, or mechanical failure can cause faults in these detectors. A faulty detector could cause the removal of 200-500 healthy wheels, resulting in unnecessary repair expenses.

Hence, the client wanted to:

  • Build a health monitoring system for WILD detectors
  • Develop an interactive tool for early identification of faulty detectors
  • Develop a reporting system for periodic review of detectors

Challenges

  • Large data volume – 100GB data over a 3-year period
  • Seasonal variations in traffic and weather patterns pose estimation challenges
  • Solution integration with existing infrastructure for quick and actionable results

Solution Methodology 

  • Reduced ~100GB data to 100MB by converting raw data into hourly alerts and summarizing hourly alerts to a weekly level
  • Clustered detectors into homogenous groups by location, climate, load, car type, and speed
  • Created metric alerts per wheel (APW) and normalized traffic using a 3-year average APW value for each site
  • Identified outliers leveraging control charts and leveraged Z-score based detector scoring system to identify faulty detectors
  • Refreshed benchmark every week based on new data

Business Impact

  • Saved over USD1 M in annual wheel replacement costs resulting from faulty WILD detectors
  • Developed a comprehensive system that delivers weekly reports via email and has a Tableau-based interactive UI with drill-down capabilities
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