Model-Based Fault Diagnosis and Prognostics in Hybrid Systems
Summary
Hybrid systems, characterised by the coexistence of continuous dynamics and discrete mode changes, pose significant challenges for ensuring operational safety and reliability in sectors such as aerospace, automotive, energy and advanced manufacturing. Model-based fault diagnosis employs mathematical and graphical representations of system behaviour—often through bond graphs or hybrid state machines—to detect, isolate and characterise faults by comparing real-time measurements with model predictions. Prognostics extends this paradigm by modelling degradation processes and estimating remaining useful life, thus enabling condition-based maintenance and the avoidance of unplanned downtime. Central to these methodologies are analytical redundancy relations and fault signature matrices that translate discrepancies into diagnostic decisions. Recent advances incorporate stochastic degradation models, such as improved Wiener processes, and formal frameworks like particle Petri nets, which account for uncertainty in both system knowledge and measurement noise. The integration of these approaches supports the development of digital twins, offering a comprehensive platform for active fault interrogation, adaptive thresholding and sequential diagnosis. By bridging rigorous theoretical foundations with practical algorithms, model-based strategies in hybrid systems deliver global impact through enhanced safety, reduced lifecycle costs and optimised maintenance schedules.
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Model-Based Fault Diagnosis and Prognostics in Hybrid Systems publication trend
The graph below shows the total number of articles in model-based fault diagnosis and prognostics in hybrid systems across all publications each year (not limited to Nature Index journals).
Technical terms
Hybrid system: A dynamic system featuring both continuous evolution and discrete transitions between operational modes.
Model-based fault diagnosis: A process of detecting and isolating system faults by comparing sensor data with outputs predicted by a mathematical model.
Prognostics: The estimation of a component’s remaining useful life and future health state based on degradation models.
Analytical redundancy relation: A model-derived equation that remains zero under fault-free conditions and deviates when faults occur.
Bond graph: A unified graphical representation of energy exchange across multi-domain physical systems, enabling systematic model derivation.
Particle Petri net: A hybrid formalism combining Petri nets with particle filtering to estimate system states and health under uncertainty.
Wiener process: A continuous-time stochastic process used to characterise random degradation trajectories in prognostic modelling.
References
- Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process. Actuators (2021).
- Health Monitoring and Prognosis of Hybrid Systems. Annual Conference of the PHM Society (2013).
- Health Monitoring of Hybrid Systems Using Hybrid Particle Petri Nets. Annual Conference of the PHM Society (2014).
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