MEC2812 · Machine learning in industrial systems

Overview

This unit introduces you to the principles and applications of machine learning in industrial systems, with a focus on solving real-world engineering problems using data-driven approaches. As modern industries increasingly adopt intelligent and automated systems, machine learning has become a core enabling technology for enhancing efficiency, reliability, safety, and decision-making across manufacturing, infrastructure, energy, and process industries. You will explore the integration of fundamental industrial and machinery systems knowledge with introductory machine learning (ML) techniques. You will develop a foundational understanding of key machine learning methods, including supervised and unsupervised learning, feature extraction, model training, and performance evaluation. Through industry-motivated case studies, you will examine applications such as predictive maintenance, fault detection and diagnosis, quality inspection, condition monitoring, and process optimisation. By the end of the unit, you will be equipped with the skills to critically evaluate machine learning solutions and apply them effectively within industrial engineering contexts.

Offerings

Assessment

The Handbook lists an examination for this unit.

Continuous assessment: 50% Final assessment: 50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Describe fundamental machinery systems, sensors, and operational issues.
2. Apply basic machine learning algorithms to analyze operational machinery data.
3. Extract and interpret features from machinery datasets for predictive insights.
4. Develop and evaluate simple ML-based solutions for machinery monitoring.

Workload

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.

Student experience, not official rules. Nothing here changes what the Handbook says.