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Risk has become a key concept in modern society. Growing concern about the environment and a number of disasters have served to focus attention on the hazards and risks involved in a wide range of activities from offshore oil production to rail and air transport; from the design of football stadia to the operation of chemical plants and environmental protection.
Today there is a wide range of techniques available to assess risk and reliability, both in relation to safety and in the wider sense. These techniques now underpin new legislation on safety and have relevance over a broad spectrum of activities, including environmental and other systems, where risk and reliability are key concerns.
The MSc/PG Diploma course in Safety, Risk and Reliability Engineering is designed to give a thorough understanding of these techniques and experience of their application to a variety of real-world problems. It aims to provide students with an understanding of safety, risk and reliability engineering in both a qualitative and quantitative manner, and to develop the skills to apply this understanding. The course will also introduce students to recent developments in analytical techniques, e.g. computer modelling of risk, reliability and safety problems.
If you are interested in our courses you can join our Linked In group Safety Courses at Heriot-Watt where you can link up with other applicants, current and previous students and potential employers. Applying to join needs approval from the group owner which may take a week or two.
The MSc/Postgraduate Diploma in Safety, Risk and Reliability Engineering, led by Professor Guy Walker, is only available by attendance-free distance learning.
The programme comprises eight taught courses. Heriot-Watt Online students must first take exams in two courses of the programme, Human Factors Methods and Learning from Disasters. Based on the results from these courses students continue on the programme at MSc or at PG Diploma level.
All courses have written examinations and some have compulsory coursework elements. MSc students are also required to complete a Masters dissertation.
|Semester 1||Semester 2||Semester 3|
|Human Factors Methods*||Human Factors Methods*||Human Factors Methods*|
|Learning from Disasters*||Learning from Disasters*||Learning from Disasters*|
|Data Analysis and Simulation||Environmental Impact Assessment||Data Analysis and Simulation|
|Risk Assessment and Safety Management||Fire Safety, Explosions and Process Safety||Risk Assessment and Safety Management|
|Systems Reliability||Safety, Risk and Reliability||Safety, Risk and Reliability|
*Human Factors Methods and Learning from Disasters are the online entry courses for this Programme. Online students must successfully complete these two courses before continuing with the remaining six taught courses.
Please find below the course descriptions. For more information on courses, please contact the Programme Leader.
Semester 1 (mandatory)
This courses aims to give students an appreciation of risk from individual and societal perspectives as well as understanding the basic principles of risk assessment and modelling and how safety management works in practice. Subjects include:
The concept and perceptions of hazards and risk. Risk attitudes and impact on decision-making; Interpretations of probability; Quantitative and qualitative aspects of risk; Modelling of decision making under conditions of risk; Inherent Safety; HAZOP; Safety management systems such as BS EN ISO 18000 series and other standards; Application of Safety Management Systems to failed systems and as a preventative tool.
Semester 1 (mandatory)
Gives an understanding of the qualitative and quantitative techniques that are used in the reliability, availability and maintainability analysis of all types of engineering systems. The syllabus is:
Basic concepts of reliability, availability and maintainability; Failure rates, failure modes, and reliability data; Reliability of systems by reliability block diagram analysis of series and parallel systems; Reliability Centred Maintenance, including replacement strategy, and inspection of standby systems; Markov modelling of system failures; Probabilistic safety analysis, based on Failure Modes Effects and Criticality Analysis, Event trees and Fault trees.
Semester 1 or 2 or 3 (mandatory)
Gives students an in depth understanding of some of the classic disasters and their consequences by using a range of practical accident investigation techniques. Students will learn to analyse complex histories in order to find the underlying root cause. Topics covered:
Accident models; Root cause and accident analysis techniques concentrating on events and causal factors analysis, barrier analysis, change analysis and the management oversight and risk tree; Review a number of famous disasters including Piper Alpha, Herald of Free Enterprise, Bhopal, Clapham Junction etc.; Identify lessons learned from these disasters; Review some of the major safety lessons from historical disasters; Analyse a real disaster in detail using a number of practical techniques
Semester 2 or 3 (mandatory)
This course aims to provide the students with an appreciation and understanding of the basic principles of structural reliability theory. It provides an introduction to concepts of structural safety and risk, as well as probability theory and probability distributions.
Specific topics covered in the course syllabus include: Probabilistic modelling of strength and loads; first order second moment and first order reliability methods; reliability-based code calibration; Monte-Carlo simulation and variance reduction techniques; Introduction to causes of structural deterioration (corrosion, fatigue and fracture); risk based inspection strategies using Bayesian methods.
Semester 2 (mandatory)
Introduces students to the basic principles of fire safety science and engineering, and develops skills in associated modelling leading to an understanding of principal fire/explosion related issues in process safety. Subjects include:
Objectives of fire safety science and engineering; Fire chemistry: stoichiometric burning, ignition, flammability limits; Mechanisms of heat transfer; The burning process; flashpoint, firepoint, flame spread; Fires in enclosures; computer-based models of fire development. Flashover & backdraught; Life threat, human behaviour, evacuation; Fire severity & fire resistance. Probabilistic modelling; Explosions: deflagrations, detonations, fire-balls; Fire related aspects of process safety. Piper Alpha disaster.
Semester 1 (mandatory)
This course develops knowledge of statistical data analysis and its application in engineering and science and introduces the concepts of using simulation techniques for analysis of complex systems. It also teaches linear optimisation techniques and the ability to apply them to solve simple problems.
Topics covered: Introduction to statistics; Basics of probability theory; Probability distributions; Sampling and confidence intervals; Hypothesis testing; Data correlation and regression analysis; Random number generation; Simulation and modelling; Elements of queuing theory; Introduction to optimisation techniques.
Semester 1 or 2 or 3 (mandatory)
This course will equip students from academic and/or industrial backgrounds with knowledge on, and the means to deploy, a wide range of specialist human factors techniques. The emphasis is on method selection, application, combination and integration within existing business practices. Students will develop a critical awareness of what methods exist, how to apply them in practice and their principle benefits and limitations.
The syllabus includes: Introduction to human factors problems and human factors methods; Task analysis; Cognitive task analysis; Human error identification; Situation awareness assessment; Mental workload assessment; Team assessment; Interface analysis; Design methods; Performance time prediction; Method integration; Human factors integration.
Semester 2 (mandatory)
Provides students with the knowledge and understanding of the principles and processes of the Environmental Impact Assessment. By the end of the course, the student should be familiar with the European EIA legislation and its translation into the Scottish planning system, and be able to demonstrate an understanding of the EIA process, the tools and the agents involved in an EIA and the possible problems with using EIA as a decision making tool. . It is also intended that the student will be able to appreciate the purpose of the EIA process from a number of perspectives; that of a developer, an EIA practitioner and a policy maker.
Topics include: Introduction to EIA; European EIA Legislation; Screening and Scoping; Baseline Studies, Analysis and Prediction of Impact; Consultation, Review and Monitoring; Beyond EIA: Strategic and Social Impact Assessment
MSc students are also required to complete an individual project (dissertation). This course has a stronger engineering bias and you should only attempt this if you have done some University level mathematics or equivalent. Otherwise the Safety and Risk Management course might be more appropriate.
For the project component of the course distance learners are likely to develop something based in their country of residence with advice and supervision from staff in the School. This may well include work with a local company or may involve independent study. Individual arrangements will be set up with each student.
Applicants to the MSc programme will normally:
Heriot-Watt Online students must first take exams in two courses of the programme, Human Factors Methods and Learning from Disasters. Based on the results for these entry courses students will continue on the programme at MSc or at PG Diploma level.
Students will additionally have access to the short course 'Introduction to Digital Study and Academic Skills', to prepare for postgraduate study.
Applicants will need a good English Language ability to succeed in their programme. Those for whom English is not their first language should have,
Applicants will need good mathematical ability to succeed on their Programmes. Students should have experience in university level mathematics of the sort commonly encountered on engineering degrees (e.g. a pass, or passes, in previous university level maths-based courses/modules) and be confident in further development of skills in statistical methods, simulation and modelling, and linear optimisation techniques. Please refer to the course guide for Data Analysis and Simulation
We are committed to providing study opportunities to applicants who have a wide range of prior experiences through Recognition of Prior Learning (RPL). For more information on RPL, please contact the Online Admissions Team (email@example.com) ahead of application. We can only consider requests for RPL at the time of application to a course of study.
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