| Unit name | Ultrasonic NDT and Data Analysis |
|---|---|
| Unit code | EEMEM0020 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Professor. Croxford |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Electrical, Electronic and Mechanical Engineering |
| Faculty | Faculty of Engineering |
Why is this unit important?
Ultrasonic methods are among the most important tools for Non-Destructive testing (NDT). In addition NDT is increasingly producing very large quantities of data whose interpretation is becoming increasingly challenging. This unit covers the fundamental science of ultrasonic and acoustic wave propagation and the application of this science to NDT. We explore a series of techniques for data analysis. From standard signal processing techniques to the application of machine learning. This leaves students well equipped to understand one of the most common NDT techniques and its application, and have a strong picture of the state of the art and future direction of data processing.
How does this unit fit into your programme of study?
There are 6 widely employed fundamental NDT techniques that all practitioners in the field must be familiar with. This unit gives that grounding in one of the 6. In addition as the quantity of data produced by inspections continues to rise so the need for a grounding in how to process and use this data is becoming increasingly important. Together these two activities deliver that fundamental experience.
An overview of content
This unit will provide you with the knowledge and application experience to understand Ultrasonic wave propagation, how experimental ultrasound systems are designed, simulation approaches for ultrasonic systems, the application of phased arrays and inspection design. It will explore the most appropriate signal processing approaches available for NDT practitioners and how and where machine learning approaches may be applied to data analysis problems in NDT.
How will students, personally be different as a result of the unit?
Students will know a range of ultrasonic techniques and how they are applied in practise. They will understand how signal processing may be used to improve measurements and how large quantities of data may be processed in semi and fully automatic NDT system.
They will be able to design inspections from briefing documents and select appropriate ML models and training approaches for automatic data analysis.
They will have developed programming skills directly applicable to NDT problems.
Learning outcomes
Having completed this unit, you will be able to:
The unit is delivered through a combination of 10 x 1 hour lectures on ultrasonic techniques, 10 x 1 hour lectures on data analysis and computer classes. These are delivered over two intensive teaching weeks. Lectures include demonstrations of both the experimental state of the art and data science SoA.
The structured exercises lead the students from no knowledge of the field to the final assessments in a gradual escalation. These exercise are designed to encourage the students to explore different approaches and learn through problem based methods that are directly industrially relevant.
Tasks which help you learn and prepare for summative tasks
The unit is assessed through a series of computing exercises of increasing complexity. These provide a clear path from initial ability to the final summative assessment. These are supported by formative assessment through informal discussions during completion to ensure students understand the end goal and how they will get there. There is a formal formative assessment half way through the course to give students explicit feedback on how they are performing towards the end goal.
Tasks which count towards your unit mark
There are two summative tasks, both to be submitted individually:
These are both in the form of short reports designed to mimic those typically produced in industry.
When assessment does not go to plan
Re-assessment takes the same form as the original summative assessment
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EEMEM0020).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
