Risk Analysis & Reliability Engineering

The course duration is 2 days and will consist of both theoretical and practical applications of risk and reliability engineering. The course will introduce the fundamentals of Risk analysis and reliability engineering. Quantity risk analysis, basic statistics, distribution of failure, reliability of failure, consequences of failure and Monte Carlo simulations (MCS).

On successful completion of this course, you will be able to: 1) identify and analyse the concepts and principles of risk and reliability engineering and their potential applications to different engineering problems. 2) Assess and analyse appropriate approaches to the collection and interpretation of data in the application of risk and reliability engineering methods. 3) Evaluate and select appropriate techniques and tools for qualitative and quantitative risk analysis and reliability assessment. 4) Analyse and evaluate failure distributions, failure likelihood and potential consequences and develop solutions for control/mitigation of risks.

Why should we do a risk and reliability analysis?

• Enable good decision-making in the presence of uncertainty.

• Demonstrate that all risks are identified and appropriately mitigated or reduced (ALARP)

• Moral- risk of death or serious injury (Safety)

• Legal- comply with regulators. (UK-Health and Safety Executive)

• Economic- profit, return for investors/ Shareholders.

WHO SHOULD ATTEND?

This training is suitable for mechanical, process, and energy engineers and professionals who want to obtain knowledge on applied aspects of risk and reliability engineering.

COST

The registration fee of the workshop is £695 plus VAT(VAT UK only) which includes course notes.

PAYMENT

We will send you an invoice for the course fee after you have registered on the course. The payment can be made via bank transfer or online credit/debit card payment. If you need any further information, please contact us by email: info@mam.engineer 

PROGRAMME

LECTURER BIO

Dr Rosemary McNiece

Dr Rosie McNiece is a senior lecturer in statistics and data science at Kingston University, London, with over 20 years’ experience in education, training and research gained in academia and across various industries and professional bodies. As a committed educator she is passionate about promoting high quality data exploration and meaningful data analysis, applying statistical methodologies and machine learning approaches to achieve data driven solutions to complex problems. She leads a research group working on novel applications of statistical and analytical methods, with a particular focus on achieving data driven solutions from large and complex routinely collected national survey data. She has successfully delivered and supervised research projects in education, transport and health care monitoring.

She is a fellow of the Royal Statistical Society, an External Research Associate for the General Medical Council, an active participate in ‘Women in Data’ and has previously held advisory roles with professional bodies such as the Health Research Authority and is an experienced peer reviewer.

Dr Donya Hajializadeh

Dr Donya Hajializadeh is a chartered engineer and senior lecturer at the University of Surrey, with more than a decade of experience in industry- focused research in the areas of structure and infrastructure health monitoring, application of machine learning approaches in data-driven damage identification systems andrisk, reliability and resilience-focused decision- making in transport infrastructure and systems. As project lead, co-investigator, technical lead/co-lead, and principal/co-supervisor, she has worked and successfully delivered over 50 commercial and research projects on time and on budget. She is an active member of several professional bodies, including the Institution of Civil Engineers, the Institution of Structural Engineers, the Institution of Engineers of Ireland and the Women’s Engineering Society.