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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