WM9G9-15 System Reliability and Diagnosis
Introductory description
The module will investigate the way in which quality and reliability techniques can be used to guarantee the quality of manufacture. Conventional techniques associated with quality, reliability and maintenance will be introduced and used to quantify and diagnose common issues. The role of embedded intelligence to capture, process and share factory events and the role of data sciences in supporting this will be introduced through class-based exploratory exercises.
Module aims
To provide students with the means to evaluate technological risks associated with building and maintaining conventional and cyber-manufacturing systems and propose means to mitigate such risk to create high quality, reliable solutions.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Quality Techniques for assessing process performance;
Six-Sigma;
Failure Mode Effect and Criticality Analysis, Fault Tree Analysis
Lifetime data analysis
Maintenance methods
Asset Management
All above in context of Industry 4.0 and cyber-manufacturing risk
Learning outcomes
By the end of the module, students should be able to:
- Examine the cyber-specific risks associated with the use of cyber-manufacturing
- Evaluate techniques and methods that effect system reliability, maintenance and operational performance
- Examine how life time data analysis can aid manufacturing systems maintenance planning
- Appraise the application of quality tools for process capability and process control.
Indicative reading list
Introduction to Statistical Quality Control, Montgomery, Douglas C. John Wiley, 2013
Reliability Engineering . Kailash C. Kapur and Michael Pecht, Wiley 2014;
Reliability centered maintenance (RCM): implementation made simple, Neil Bloom, McGraw-Hill, 2006;
Reliability Modeling and Analysis of Smart Power Systems, Karki Billinton & Verma (eds), Springer, 2014;
A Hands-on Introduction to Data Science. Chirag Shah, Cambridge University Press, 2020;
Cybersecurity of industrial systems , Flaus, Jean-Marie, ISTE, 2019;
Cyber Defence in the Age of AI, Smart Societies and Augmented Humanity. Jahankhani, Kendzierskyj, Chelvachandran & Ibarra (eds), Springer, 2020.
Subject specific skills
Use quality techniques such as SPC and SIx-Sigma to diagnose process performance and capability;
Analyse the potential sources and effect of faults in manufacturing processes;
Perform simple analysis of fault data using a relevant data-science technique.
Transferable skills
Dealing with complex issues both systematically and creatively, make sound judgements in the absence of complete data, and communicate conclusions clearly to specialist and non-specialist audiences;
Demonstrate self-direction and originality in tackling and solving problems, and act autonomously in planning and implementing tasks at a professional or equivalent level;
Advance their knowledge and understanding, by developing new technical skills;
Independent learning ability required for continuing professional development.
Study time
| Type | Required |
|---|---|
| Lectures | 6 sessions of 1 hour (4%) |
| Seminars | 24 sessions of 1 hour (16%) |
| Demonstrations | (0%) |
| Practical classes | 8 sessions of 1 hour (5%) |
| Supervised practical classes | (0%) |
| Online learning (scheduled sessions) | 20 sessions of 1 hour (13%) |
| Online learning (independent) | 20 sessions of 1 hour (13%) |
| Private study | 12 hours (8%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
reviewing moodle material and reading list
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | |
|---|---|---|
| Conceptual Design using Machine Intelligence for Systems Support | 20% | 4 hours |
|
Working in small teams, apply the module's core concepts and carry out preliminary research to conceive the conceptual design of a resilient cyber-manufacturing system |
||
| Post Module Assignment | 80% | 56 hours |
|
Select an existing firm that manufactures a complex product and based on this develop a balanced argument for or against the use of intelligent devices to support the maintenance of system integrity, reliability and performance. |
||
Assessment group R1
| Weighting | Study time | |
|---|---|---|
| Post Module Resubmission Assignment | 100% | |
|
Select an existing firm that manufactures a complex product and based on this develop a balanced argument for or against the use of intelligent devices to support the maintenance of system integrity, reliability and performance. |
||
Feedback on assessment
The presentation will receive written feedback and a checklist showing strengths and weaknesses;
The post module assignment will have written feedback
There is currently no information about the courses for which this module is core or optional.