Integrated Health Management in Aerospace Systems

The field of integrated health management in aerospace systems unites sensor networks, data analytics, modelling and decision-support tools to monitor, diagnose and predict the state of health of aircraft and spacecraft. By fusing information from multiple subsystems—such as propulsion, environmental control, electrical power and avionics—these frameworks deliver timely indications of incipient faults, estimate remaining useful life of critical components and guide maintenance and mission adjustments. Recent advances in digital twin technology and artificial intelligence have enabled more granular modelling of complex system interactions, while hybrid model-based and data-driven methods have improved the reliability and accuracy of diagnostics and prognostics. This holistic approach reduces unscheduled maintenance, cuts lifecycle costs and enhances safety and mission success. It supports a spectrum of applications from manned commercial airliners to unmanned aerial vehicles and distributed satellite constellations, offering dynamic mission management that adapts flight or mission profiles in response to real-time health predictions. Growing regulatory and industry emphasis on predictive maintenance, condition-based maintenance and autonomous operations has driven collaboration across academia, operators and manufacturers, highlighting the global significance of integrated health management in future aerospace endeavours.

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Integrated Health Management in Aerospace Systems publication trend

The graph below shows the total number of articles in integrated health management in aerospace systems across all publications each year (not limited to Nature Index journals).

Technical terms

Integrated Vehicle Health Management (IVHM): A framework that consolidates diagnostics, prognostics and decision-support across all vehicle subsystems to optimise maintenance and mission outcomes.

Integrated System Health Management (ISHM): A system-level approach to monitor, diagnose and predict the health state of components and subsystems, often combining model-based and data-driven methods.

Remaining Useful Life (RUL): The estimated time or usage duration before a component or system is expected to no longer perform satisfactorily.

Prognostics: Techniques used to predict the future health state or failure time of a system based on current and historical data.

Digital Twin: A virtual representation of a physical system that mirrors its behaviour and health state in real time, enabling simulation and advanced fault analysis.

Condition-Based Maintenance (CBM): An approach that schedules maintenance tasks based on the actual condition of equipment as determined by monitoring and diagnostics rather than fixed intervals.

References

1. The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in Aerospace Sciences (2019).
2. Advances in Integrated System Health Management for mission-essential and safety-critical aerospace applications. Progress in Aerospace Sciences (2022).
3. Platform health management for aircraft maintenance – a review. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering (2024).