Engineering Analysis and Modelling
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Prerequisites
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
This unit introduces students to advanced techniques for analysing random data and processes, which are essential for modern engineering applications. As the monitoring of systems, machines, and environmental processes becomes increasingly vital in engineering, understanding and applying appropriate analytical methods is critical for Mechanical Engineers. This unit explores various dynamic processes that involve random and non-deterministic behaviours, such as seismic motion, vehicle vibrations and acoustics. Students will study key techniques, including statistical distributions, moving statistics, frequency analysis (Fourier Analysis and the Fourier Transform), frequency response functions, digital sampling and filtering, and the Laplace Transform. These methods will equip students with the tools needed to analyse complex engineering data and optimise the performance and reliability of engineering systems.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Adapt frequency domain analysis techniques to various signal types and interpret the results accurately;
- Evaluate the frequency response functions of mechanical systems using Fourier analysis;
- Devise appropriate strategies for sampling diverse signal types and implementing suitable digital filters;
- Conduct statistical analysis on a range of data sets to extract meaningful insights;
- Utilise Laplace transform techniques to solve differential equations related to mechanical systems; and
- Develop and validate computer code for performing the above analyses and generate clear, technical reports both individually and in collaborative team settings.
Assessment
For Melbourne campuses
Students will work in groups to prepare the portfolios.
Assessment type: Portfolio | Grade: 30%
Algorithm development and data analysis portfolio (Group) (2000 words)
Assessment type: Test | Grade: 35%
In-class invigilated test 1 (90 mins) (Individual)
Assessment type: Test | Grade: 35%
In-class invigilated test 2 (90mins) (Individual)
Portfolio: The portfolio includes an evaluation of the contributions made by each member of the team to facilitate the assessment of students’ achievement of learning outcomes. The portfolio’s integrity is maintained through progressive submission checkpoints, code originality verification, and in-class monitoring of each student’s contributions. Students collaborate to design, implement, and document algorithms and data analysis workflows addressing random data and dynamic system problems. The portfolio includes practical coding exercises, analytical reports, and reflections on group contributions and applied problem-solving. Explicit evaluation of individual contributions further supports the assessment and assurance of each student’s achievement of learning outcomes.
Tests: All tests are invigilated under secure conditions to ensure authenticity and individual accountability. Test 1 evaluates students’ understanding of statistical methods, introductory frequency domain analysis and Fourier techniques, and digital sampling, testing their ability to interpret and apply analytical results. Test 2 focuses on dual channel Fourier analysis, Laplace transforms, transfer functions, and data interpretation, assessing students’ capacity to integrate theoretical knowledge and computational skills in solving engineering problems.
Required reading
Refer to VU Collaborate for recommenced reading and other resources.
As part of a course
This unit is studied as part of the following course(s):