Cloud-Based Big Data Platform for Predictive Maintenance and Operational Optimization of In-Flight Connectivity Systems

The client faces frequent satellite antenna malfunctions leading to degraded internet performance, penalties, and unnecessary equipment downtime marked by high no-faultfound rates. Despite known potential failure causes, these issues are difficult to predict and prevent, resulting in increased operational costs and customer dissatisfaction.

A global provider of in-flight broadband internet services operating a large fleet of commercial and private aircraft, aiming to enhance internet quality and reduce operational costs through advanced data analytics.

• Implement a scalable cloud-based data platform to aggregate and process data from multiple sources related to equipment and network performance.
• Develop predictive models using Data Science techniques to forecast equipment failures up to 30 days in advance with at least 90% accuracy.
• Identify and address root causes of equipment failures to reduce no-faultfound rates by at least 75%.
• Enhance operational decision-making through comprehensive reporting tools informing maintenance scheduling and user experience improvements.
• Reduce costs associated with penalties, unnecessary equipment servicing, and infrastructure licensing.

• End-to-end data pipeline from log collection to storage in a data lake architecture.
• Real-time streaming data processing of up to 3 TB per day.
• Integration of multiple data sources including equipment logs, weather data, and hardware parameters.
• Development of Data Science models such as Gaussian Mixture Models and Regression Analysis for failure prediction.
• Correlation analysis between equipment status, environmental conditions, and operational parameters.
• Interactive reporting dashboards providing insights into equipment health, operational KPIs, and customer experience metrics.
• Proactive alerts for maintenance teams based on predictive analytics outputs.
• User experience analysis focusing on connection initiation timing during the first 15 seconds of connectivity.

• Equipment logs and telemetry data streams
• Weather and environmental data APIs
• Operational and financial data systems
• User feedback and connection quality metrics

• System capable of processing up to 3 TB of streaming data daily with minimal latency
• High system availability and fault tolerance in cloud environment
• Data security and compliance with relevant standards
• Scalable architecture to accommodate growing data sources and volume
• Maintainability and extensibility for future feature integration

The implementation of a cloud-based big data analytics platform will enable the client to predict equipment failures with 90% accuracy, allowing for maintenance to be scheduled proactively, thereby reducing no-faultfound rates by approximately 75%. This will lead to significant cost savings by decreasing unnecessary servicing and penalties, while improving customer satisfaction through enhanced internet service quality and reduced downtime. The system will foster more informed decision-making and operational efficiency, driving overall service excellence and competitive advantage.