TEK-3018 Advanced techniques for risk and reliability analysis - 10 stp

The complexity of engineering systems and critical infrastructures in today’s industry is ever increasing. The course will give an overview and deep understanding of modelling and analysis of risk and reliability of such complex technical systems. The impact of Arctic conditions on system performance will be discussed.

The course content is organised in the following modules:

Module 1 - Markov reliability and availability analysis

Module 2 - Covariate-based survival models

• Time-dependent and time-independent covariates
• Application of proportional Hazard (PH) and Accelerated Failure Time (AFT) models for reliability and maintainability
• Observed and unobserved covariates

Module 3 - Application of Bayesian statistics and Bayesian networks in risk and reliability analysis

Module 4 - Monte Carlo simulation (MCS) techniques for RAM analysis

• Indirect and direct sampling methods
• MCS for reliability and failure probabilities
• MCS for system availability

Module 5 - Expert judgement process for risk and reliability analysis

• Introduction to expert judgement process
• Expert judgement steps
• Combining expert distributions
• Analysing uncertainties associated with expert judgements

Module 6 - Importance Measures

• Birnbaum’s measure
• Criticality importance
• Fussell-Vesely importance
• Risk Achievement Worth and Risk Reduction Worth
• Differential importance measures
• Importance measure for multi-state system

Module 7 - Big data analytics, Application of Neural Networks in risk and reliability analysis.

Knowledge:

The student...

• understand the concept of complex system’s in risk and reliability
• has deep knowledge of key elements, of system risk and RAM, and challenges associated with risk and reliability analysis of today’s complex technical systems
• has advanced knowledge on research and development work in the field of risk and RAM analysis of technical systems using different tools and techniques, focused on the impact of Arctic physical conditions.
• has the ability to update his/her knowledge within the key areas of analysing the risk, safety, and RAM performance of complex engineering systems.

Skills:

The student...

• can model and analyse the risks as well as RAM performance of complex technical systems
• can analyse the consequences of uncertainties in different parts of the models
• can propose solutions the issue of lack of historical data using simulation techniques and expert judgement process, and at the same time handling the big data resources (e.g. collected by condition monitoring systems)
• can develop and analyse impact of environmental conditions on system performance (e.g., Arctic harsh physical conditions)
• can apply his/her academic knowledge and relevant results from available research and development work to practical and theoretical issues and thus make informed judgements/inputs related to the risk and safety aspects of complex technical systems, and their RAM performance
• can reflect on his/her own professional practice and adjust it under supervision

General competence:

The student...

• has deep insight into relevant professional and ethical issues
• can communicate central subject matter such as theories, issues and solutions, can exchange views and experiences with others with background in risk and reliability analysis of complex systems
• can formulate necessary studies for performing concrete risk and reliability analysis for complex technical systems
• can contribute to new thinking and innovation processes
• can analyse the information in connection with risk and reliability performance of complex systems

Lectures, group works, using MATLAB and STATA software packages, as well as mandatory homework.

Around 30 hours lectures and 10 hours seminars /and exercises.

The date for the exam can be changed. The final date will be announced at your faculty early in May and early in November.