Intelligent health and mission management systems for aerospace and defence applications

Intelligent Health and Mission Management (IHMM) is the next major evolution in the line of system health management concepts in the aerospace, land and maritime industries. It exceeds the maintenance and logistics support benefits of traditional health management systems by introducing predictive integrity and dynamic mission management. This involves utilizing a combination of real-time measurements from distributed sensor networks as well as high-fidelity models of subsystems and faults to predict the state of health of systems and enable subsequent reconfiguration of systems and replanning of mission activities. Furthermore, dynamic mission management and real-time decision support are novel aspects of health management systems that are enabled by the enhanced health monitoring and health prediction capabilities brought forward by IHMM. These capabilities allow IHMM systems to assume a safety-critical and mission-essential role in the next generation of trusted autonomous aerospace and defence systems. This research project addresses the development of a number of diagnostic and prognostic tools to be utilized in such health management system frameworks for three distinct case studies. The case studies are selected to analyse the different IHMM development requirements across conventional, semi-autonomous and autonomous systems. A variety of Artificial Intelligence (AI) and Machine Learning (ML) tools are utilized in the development of the diagnostic and prognostic algorithms. The limitations of the inference processes developed in each case study are considered. These are mainly associated with the fidelity and assumptions made in the models used to represent the behaviour of systems, as systems operating in the real-world are subject to many external environmental and operational factors with complex interactions that are difficult to account for in physical models. This suggests that the optimal approach is to capitalize on the complementary advantages of both model-based and data-driven approaches to maximise the accuracy, timeliness and reliability of integrity assessments as well as predictions. The integration of IHMM frameworks within selected case-studies are presented, considering the flow of sensor measurements, data and commands. The benefits of the mission reconfiguration capability brought forward by IHMM in response to a detected or predicted fault or performance degradation is demonstrated. This supports future development of more complex forms of IHMM based mission reconfiguration.