Objective
- Implement a predictive model for estimating Remaining Brake Life.
- Focus on wear consumption metrics to develop predictive equations through Regression models.
- Forecast Brake Wear % remaining for each of the four brake assemblies on an aircraft.
- Facilitate the calculation of replacement dates based on the predictive trend equation.
- Integrate automatic notifications/alerts for users when brake wear reaches pre-defined threshold values, with user-settable thresholds.
Tools/Technology
Hadoop: Efficient management and processing of extensive data sets.
PRISM: Comprehensive analytics and streamlined data visualization.
R: Statistical analysis and development of predictive regression models.
QlikSense: Interactive and user-friendly data visualization for enhanced insights.
Benefits Achieved
Optimized Brake Unit Management:
Enable proactive planning for maintenance activities based on accurate wear predictions.
Reduced Unscheduled Maintenance:
Achieved a notable 10% reduction in unscheduled maintenance, optimizing operational efficiency.
Enhanced Operational Reliability:
Mitigated operational risks, substantially lowering the likelihood of aircraft becoming operationally unavailable due to unexpected brake issues.
The Results
By combining advanced analytics techniques with Hadoop, PRISM, R, and QlikSense, Mathesis Labs successfully transformed aircraft maintenance practices. The predictive analytics solution not only reduced maintenance costs but also elevated the overall operational readiness of aircraft, ensuring a safer and more efficient flying experience. The ability to predict remaining brake life provided the airline industry with a valuable tool for proactive maintenance planning and risk mitigation.
Aircraft Health Monitoring System (AHMS) as a Service
Business Need:
- Mathesis Labs to build, host, operate, and offer AHMS as a service to OEM’s customers – aero operators.
- Develop a state-of-the-art tool to maximize aircraft utilization and reduce operating costs.
- Create a flexible and scalable platform services offering for open selection.
- Minimize the impact of new aircraft model introduction into the existing fleet.
Solution Highlights
Remote Diagnostics:
Enable remote diagnostics of health, performance monitoring, and operational reporting of the aircraft.
Basic Analytics:
Conduct extensive analysis supporting trending, usage monitoring, etc., for a single aircraft.
Provide operators and aircraft manufacturers with analysis of various parameters.
Advanced Analytics:
Utilize applied statistics for predictive maintenance with ‘predictive and prescriptive ability‘ (diagnostics & prognostics).
Benefits
Multi-Tenant Architecture:
Support multiple tenants, allowing concurrent use by various OEMs and aero operators.
Analytics as a Service:
Offer analytics solutions as a service to streamline operations and decision-making.
Cloud-Based Solution:
Utilize cloud infrastructure for accessibility, scalability, and efficient data management.
Subscription-Based Pricing:
Support all variations of the CAPEX-OPEX model to cater to diverse customer preferences.
The Aircraft Health Monitoring System (AHMS) as a Service by Mathesis Labs stands as an innovative solution, providing a comprehensive set of features to optimize aircraft operations and enhance overall efficiency. The incorporation of advanced analytics and cloud-based architecture ensures a modern and adaptable approach to aircraft health monitoring.