| Semester 1, 2023 Online | |
| Units : | 1 |
| School or Department : | School of Engineering |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Course Coordinator:
Overview
An instrument is an Information processing machine involving sensing (usually analogue); signal processing (analogue and digital); reference to a scale of measurement or a standard; and, display or actuation. Although modern instruments are mostly implemented using electronic technology, their functionality is determined largely by embedded software. The physics of the sensing interface remains fundamental. Design of an optimal instrument (or instrumentation system) to meet a new measurement requirement involves the formal design methodology of measurement science: it is not adequate to rely on experience alone and an "off-the-shelf" solution will usually not be available. This course will prepare students for rigorous design of measurement systems, required in industrial and processing situations, and is the logical successor to ELE3506, with a more philosophical basis.
This course does NOT present a traditional catalogue of standard measurement techniques. In consequence this is a design-oriented course which seeks to develop cross-disciplinary skills in fundamental areas including the use of the Measurement Process Algorithm; the physics, classification and selection of sensors and transducers; theory of scales and standards; signals, systems and modelling techniques; evaluation of available technologies; manufacturing; economic and management implications. Advanced topics will be drawn from: fibre optic and silicon sensors; distributed sensing; rule based and fuzzy sensing; multisensor systems and sensor fusion; intelligence and mechatronics in instruments; and tactile sensing. This course is appropriate for students with a range of backgrounds in the senior or honours years of an engineering or science degree with an appropriate electronics background.
Course learning outcomes
The course objectives define the student learning outcomes for a course. On completion of this course, students should be able to:
- analyse general measurement problems in terms of referents and measurands by means of the Measurement Process Algorithm;
- analyse and model instrumentation systems in terms of information flow;
- define and explain common instrument performance parameters including static and dynamic response;
- analyse and model transducer performance;
- evaluate alternative technologies that might be applied in the realisation of an instrument;
- select and implement major signal recovery methods and strategies for signal-to-noise improvement;
- draw up specifications and plans for the development and management of an instrumentation system;
- choose appropriate transducers and instrumentation system components in the broad areas of temperature measurement and flow measurement;
- evaluate current developments and potential future directions in sensing techniques and measurement system design.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. |
Instruments as Information Machines 1.1. The scope of measurement science and instrumentation engineering. 1.2. Measurement system architecture. 1.3. The differing roles of measurement - knowledge/calibration/control. |
5.00 |
| 2. |
Identification of the Measurement Requirement 2.1. The Measurement Process Algorithm - attributes, referents and measurands. |
5.00 |
| 3. |
Overview of Sensors and Transducers 3.1. Energy conversion, impedances 3.2. The information machine versus the energy machine. 3.3. Multi- sensitivity, influence variables. 3.4. "Latent Information". 3.5. Sensor individuality. 3.6. Sensor classification - self-generating and modulating. 3.7. Energy domains. 3.8. 2D, 3D and 4D sensor space. |
10.00 |
| 4. |
Transducer Modelling 4.1. Reasons for modelling and types of model. 4.2. Energy flow modelling and terminal relations. 4.3. Overview of mathematical techniques, FDM, FEM, applications and examples. 4.4. Models as functional parts of instruments |
10.00 |
| 5. |
Design of Measurement Systems 5.1. Philosophy, approaches, engineering design versus industrial design. 5.2. Specifications, the CAD and CAE of instruments. |
5.00 |
| 6. |
Enabling Technologies 6.1. Electrical. 6.2. Mechanical/kinematic. 6.3. Fluid/thermal. 6.4. Radiative/acoustic/ optical. |
10.00 |
| 7. |
Signal Recovery Techniques 7.1. Noise in measurement systems, white, 1/f, drift, offset. 7.2. Theory of averaging, the Boxcar, the Multipoint Averager. 7.3. Autocorrelation and crosscorrelation in instruments. 7.4. Modulation-based techniques, synchronous detection and "lock-in" techniques. |
10.00 |
| 8. |
Transducer Practice 8.1. Temperature and flow measurement |
10.00 |
| 9. | Management of Instrument Systems | 5.00 |
| 10. |
Current and Future Directions 10.1. Distributed measurement systems, field bus options. 10.2. Smart sensors, concepts, examples. 10.3. Fibre optic sensing, fibre optic fundamentals, sensing capabilities, options, examples. 10.4. Sensing for robotics, requirements, tactile sensing and imaging. 10.5. Distributed sensing; sensor fusion, concepts and requirements, introduction to fuzzy processing, robotic applications |
30.00 |
Text and materials required to be purchased or accessed
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Portfolio 1 | No | 10 | 1,2,3,9 |
| Model (theoretical) | No | 20 | 1,2,5 |
| Design A1 of 2 | No | 10 | 4,6,7,8 |
| Design A2 of 2 | No | 30 | 4,6,7,8 |
| Portfolio 2 | No | 30 | 1,2,3,4,5,6,7,8,9 |