Research Areas


Fault Diagnosis and Prognosiscoming-soon Gradual development of anomalies (i.e., deviations from the nominal condition) may alter the quasi-static behavior of human-engineered complex systems (e.g., avionic systems, sensor networks, aircraft air management system, engine control systems, building HVAC systems, energy systems, smart grids, fuel cells, hybrid electric vehicles, autonomous systems, and power plants). While the complexity remains largely hidden during the normal phase of operation of a complex system, it becomes acutely conspicuous when contributing to rare cascading and catastrophic failures. In particular, a major goal for safe and reliable operation of such complex systems is early diagnosis and prognosis of incipient faults and mitigation of fault propagation for prevention of catastrophic failures without any significant compromise of quality and performance. From these perspectives, the goal of future generation human-engineered complex systems is augmentation of the basic infrastructure with an automated diagnostic and prognostic toolbox for real-time information assessment and condition monitoring that provides sufficiently advanced warnings of emerging faults. In this regard, the research at LINKS is focused on integrating the model-based, knowledge-based and data-driven approaches into a unified PHM architecture based on multidisciplinary concepts derived from Information Theory, Symbolic Dynamics, and Machine Perception.