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Camgian Harnesses Telemetry Data to Reduce 
Maintenance Risks and Potential Downtime

Maintenance Prioritization and Risk Mitigation: 
A Customer-Centric Approach for a Global Heavy Lift OEM

Problem

  • Reduce maintenance risks and downtime: for both leased and serviced client assets

  • Empower service personnel: utilize telemetry data and insights to improve decision-making, leading 
    to increased customer satisfaction

  • Explore data beyond basic metrics: investigate the possibility of extracting deeper insights from 
    telemetry data beyond standard GPS and service history

  • Assess machine learning feasibility: analyze the potential of using machine learning to enhance 
    situational awareness, predict future risks, and integrate predictive analytics into other areas (e.g., 
    inventory management)

Solution

  • Architecture to support near-real-time data of 3000+ assets, across 30+ service centers

  • User friendly, simplified interface to fit seamlessly into workflow

  • Create cloud-base integrated database to support internal and external data sets

  • Algorithm development for anomaly detection, forecasting, predictive analytics, and prioritization 

  • Predictive modeling of major system components such as engines, turbos, transmissions, etc

Conclusion

  • Developed a prioritized maintenance dashboard: Highlights assets at the highest risk of downtime, utilizing fault codes and historical data

  • Implemented automated reporting: Daily emails to corporate management and service centers provide the prioritized maintenance table

  • Generated additional daily reports: Tracks driver behavior/operational intensity and identifies overdue service needs using telemetry data

  • Data integration for machine learning: Combined maintenance records and telemetry data to establish and validate the feasibility of predictive models

  • Created predictive models (beta): Successfully developed and deployed initial models for turbochargers and transmissions

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