Undertake predictive maintenance to maximize aircraft uptime

Overview

The Infosys Travel & Hospitality practice helps airline enterprises leverage digital tools for safe and sustainable operations. We integrate diverse data streams using scalable data architecture to capitalize on data mining tools, predictive analytics, and machine learning-based rare event and Complex Event Processing (CEP) models. A digital ecosystem facilitates predictive maintenance to minimize grounding of aircraft for servicing

Our analytical solutions collate, correlate and analyze data from various aircraft systems, including Maintenance, Repair and Overhaul (MRO) history, engineering data, aircraft utilization records, flight parameters, crew logbooks, and weather reports. Our approach enables contextual diagnostics to predict failure and identify optimum maintenance intervals. The correlation of messages and patterns helps mitigate malfunctions, while revealing factors contributing to potential maintenance issues; for example, flight routes or flying practices causing safety issues, excessive wear and tear, or structural damage.

We create a ‘digital twin’ of critical airplane parts such as the engine and landing gear. Simulation and ‘what-if’ scenarios provide insights to maximize performance as well as the lifespan of aircraft components. We integrate augmented / virtual / mixed reality into fleet management systems to boost productivity of engineering teams as well as maintenance and repair crew.

Our real-time remote equipment monitoring solutions distil knowledge from engagements with leading aircraft manufacturers. It helps us address maintenance requirements across airplane models and structural components. We align predictive maintenance programs with enterprise systems, which streamlines workforce scheduling and improves spare parts inventory management. Our holistic strategy minimizes aircraft downtime and fuel costs. Significantly, accurate insights allow airlines to rationalize capital investment via informed repair or replacement decisions to manage an aging fleet.