Risk and Reliability
Our focus is to develop methods and modelling capabilities to improve inherent component reliability, maximise system operational reliability and availability, and understand & mitigate risks to make systems safer and more resilient.
Application of risk and reliability methods in engineering domains has been in existence as a research area for over 40 years. Our research focusses on advances in assessment methods to tackle the complexities of modern-day systems – multi-functionality, dynamic operating environments, and increasing levels of dependencies and uncertainties. These issues are exacerbated by systems now being systems-of-systems, with requirements to be highly integrated across different platforms, have seamless interoperability, where maximising performance is now a complex multi-criteria decision-making process.
With a clear focus on aviation and automotive transportation, this research supports the transition to a net zero carbon future. Priorities include enabling safety in autonomous vehicles and the connected infrastructure and maximising hybrid & electrified powertrain performance through degradation analysis and health monitoring.
The research at component level, in better understanding degradation mechanisms in new emerging technologies like batteries and fuel cells, is supported by experimental lab facilities including an environmental chamber facilitating high and low temperature operating conditions.
At system level, research in novel health monitoring methods leading to increased levels of accuracy for diagnostics and improved prognostics i.e. remaining life prediction enables improved maintenance strategies and ultimate improved system performance.
Activities we address include but are not limited to:
- Component failure analysis
- Battery & fuel cell degradation
- Powertrain health monitoring
- Enhanced Model- and data-based diagnostics
- Model-, data-, expert knowledge-based system prognostic methods
- Advanced reliability model generation
- I-O-T enabled Predictive maintenance
- Optimised fleet maintenance strategies
- Risk evaluation in autonomous domains
- System performance under uncertainty
- Quantified models for transportation resilience
More about us
Academic staff involved and their primary research interests in this area:
- Professor Lisa Jackson - Techniques for enhanced reliability engineering and system safety including system failure and degradation analysis, health monitoring and transportation resiliience
- Dr Sarah Dunnett - Enhanced reliability modelling techniques, including automation and application of reliability modelling methods to novel applications
- Dr Paul Cunningham - Component degradation analysis, typically applied to composite materials
- Prof Rui Chen - Component and system degradation analysis for fuel cells
- Prof Kambiz Ebrahimi - Health monitoring of automotive systems
- Dr Thomas Steffen - Diagnostics / prognostics of technical systems using computational statistics for rare events
- Dr Miguel Martínez – Artificial intelligence and numerical approaches for modelling the behaviour of complex systems, and for predicting uncertainty in system response
- Dr Eve Zhang – Condition monitoring and fault diagnosis of industrial systems, data-driven and grey-box modelling, artificial intelligence, pattern recognition and system identification
- Dr Ashley Fly – Health monitoring, degradation modelling and onboard diagnostics of lithium-ion battery and hydrogen fuel cell systems