Unit Description and General Aims

This unit is designed to equip mechanical engineers and engineering students with essential skills and knowledge to harness the power of data analytics in various engineering contexts. Participants will learn how to collect, process, analyse, and interpret data to make informed decisions, optimize processes, and enhance the performance of mechanical systems. The unit will cover a wide range of topics from statistics and data visualization to machine learning and optimization techniques tailored specifically for engineering applications.

Learning Outcomes

On successful completion of this Unit, students are expected to be able to:

1. Assess the impact of Data-Driven Decision-Making in Engineering, Analysing Data Analytics and Key Concepts in Data Collection and Pre-processing.
   • Bloom’s Level 5
2. Create predictive models by incorporating regression analysis and develop features tailored for engineering applications.
   • Bloom’s Level 6
3. Create machine learning-based estimation models for engineering challenges, including supervised, unsupervised learning, and time series analysis.
   • Bloom’s Level 6
4. Evaluate and optimize engineering designs, justifying their choices and assessing the impact on the overall system performance.
   • Bloom’s Level 5
5. Choose, manage, and process large datasets, effectively utilize various cloud computing platforms, and implement distributed data storage solutions for mechanical data applications.
   • Bloom’s Level 6
6. Build quality control and predictive maintenance solutions for process improvement and system optimization in manufacturing
   • Bloom’s Level 6

Student assessment

Prescribed and Recommended readings

Required Textbook

Moore, D. S., McCabe, G. P., & Craig, B. A. Introduction to the Practice of Statistics, 2019, 9th Edition, W. H. Freeman.

Müller, A. C., & Guido, S. Introduction to Machine Learning with Python: A Guide for Data Scientists, 2016, O'Reilly Media.

Ravindran, S., Philips, V., & Solberg, J. Engineering Optimization: Methods and Applications, 2019, Wiley.

Reference Materials

Mayer-Schönberger, V., & Cukier, K. Big Data: A Revolution That Will Transform How We Live, Work, and Think, 2013, Houghton Mifflin Harcourt.

Unit Content

One topic is delivered per contact week:

Topic 1

Introduction to Data Analytics and Engineering Applications

• Overview of data analytics in engineering
• Importance of data-driven decision-making
• Introduction to data collection and pre-processing techniques

Topic 2

Descriptive and Inferential Statistics for Engineers

• Basic statistical concepts and measures
• Probability distributions and sampling techniques
• Hypothesis testing and confidence intervals

Topic 3 

Data Visualization for Mechanical Engineers

• Principles of effective data visualization
• Tools and techniques for creating visualizations
• Visualization case studies in mechanical engineering

Topic 4

Regression Analysis and Predictive Modelling

• Linear and nonlinear regression techniques
• Model selection and validation
• Engineering applications of predictive modelling

Topic 5

Feature Engineering and Selection

• Identifying relevant features
• Techniques for feature extraction and engineering
• Feature selection methods

Topic 6

Supervised Learning Algorithms

• Introduction to supervised learning
• Decision trees, support vector machines, and neural networks
• Practical applications in engineering

Topic 7

Unsupervised Learning and Clustering Techniques

• Clustering algorithms (K-means, hierarchical clustering)
• Dimensionality reduction techniques (PCA)
• Applications in engineering system analysis

Topic 8

Time Series Analysis and Forecasting

• Time series data exploration
• Time series forecasting methods
• Forecasting mechanical system behaviour

Topic 9

Optimization Methods for Engineering Design

• Introduction to optimization
• Optimization techniques (linear, nonlinear, integer)
• Engineering design optimization case studies

Topic 10

Big Data and Cloud Computing for Mechanical Data

• Handling large datasets
• Introduction to cloud computing platforms
• Distributed data processing and storage

Topic 11  

Quality Control and Anomaly Detection in Manufacturing

• Statistical process control (SPC)
• Quality metrics and control charts
• Detecting anomalies in manufacturing processes

Topic 12

Predictive Maintenance in Mechanical Systems

• Preventive vs. predictive maintenance
• Condition monitoring techniques
• Building predictive maintenance models
• Recent trends and future scopes

Engineers Australia

The Australian Engineering Stage 1 Competency Standards for the Professional Engineer, approved as of 2013. This table is referenced in the mapping of graduate attributes to learning outcomes and via the learning outcomes to student assessment.

Software/Hardware Used

Software: Matlab, Python, Excel

MATLAB is a versatile programming and numeric computing platform used for data analysis, simulations, and algorithm development. It is crucial for mechanical engineers as it enhances problem-solving capabilities, reduces development time, and facilitates complex calculations. Applications include control systems, signal processing, and design optimization.

MS Excel is a powerful spreadsheet software used for data analysis, visualization, and management. It is crucial for mechanical engineers as it enhances data organization, simplifies complex calculations, and supports decision-making. Applications include creating engineering reports, performing statistical analysis, and managing project data.

Python is a versatile programming language used for data analysis, automation, and simulation. It is crucial for mechanical engineers as it enhances problem-solving capabilities, reduces development time, and supports complex calculations. Applications include automating repetitive tasks, developing custom simulations, and analyzing large datasets. 

Hardware: NA

Unit Changes Based on Student Feedback