Module Catalogue 2026/27

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

This module aims to provide learners with a robust foundation in machinery fault diagnosis and prognosis (FDP) tailored for advanced manufacturing environments. After this course, students will gain an essential but significant understanding of machinery FDP, failure modes in rotating machinery, data acquisition techniques, and MATLAB applications. The curriculum includes basic signal processing techniques, such as time and frequency domain analysis, and incorporates fundamental artificial intelligence (AI) methods for fault diagnosis and prognosis. Students will be shown demonstrations of machinery health monitoring by various condition monitoring aspects. Students will also have the opportunity to consolidate their knowledge through real-world case studies, highlighting practical applications and collaborative problem-solving.

Outline Of Syllabus

Introduction to machinery fault diagnosis and prognosis (FDP)
-Background and significance of FDP
-Definitions in FDP
-Picture/video examples

Failure modes in rotation machine
-Deformation
-Lubricant
-electrical arc erosion
-misalignment
-Picture/video to show different failure modes.

Data acquisition techniques
-Computer aided data acquisition
-Wireless data acquisition
-Data recording
-Data acquisition techniques in rotating machines (Vibration, AE, Lubricant, power, microscope)
-Introduction to use MATLAB for reading and processing the collected data
-Practical examples of using data acquisition techniques.

Signal processing – Time domain analysis
-Noise
-Signal preprocessing
-Basic theory of time domain-based filtering

Signal processing– Frequency domain analysis
-Fourier transform
-Basic theory of frequency domain-based filtering
-Practical examples to show filtering results.

Signal processing– Fault detection
-Signal demodulation
-Hilbert envelope
-Morphologic envelope
-Fault detection framework
-Practical examples to show the diagnostic framework.

AI-based method - Feature extraction and fault prediction
-Introduction to statistic features
-Comparisons of different features
-Supervised learning and unsupervised learning for fault prediction

Group project
-Case 1: Bearing fault diagnosis
-Case 2: Tool condition monitoring
-Case 3: Shaft misalignment

Intended Knowledge Outcomes

Upon successful completion of this module, learners will be able to:

Demonstrate awareness of the significance, terminology, and core concepts of machinery FDP.

analyse and interpret measured data and draw conclusions (M1,M2,M3,M6)

Intended Skill Outcomes

Upon successful completion of this module, learners will be able to:

Employ data acquisition techniques, and conduct fundamental time and frequency domain signal processing techniques for machinery FDP.

Apply AI methods, including feature extraction and learning-based approaches, to conduct machinery FDP.

Collaborate and work in teams to address real-world machinery fault scenarios using a variety of case studies.

Teaching Rationale And Relationship

The module will utilize a mix of teaching methods including lectures, laboratories, interactive demonstrations, group discussions, group projects, and quizzes to ensure comprehensive understanding and skill acquisition for the students.

Assessment Rationale And Relationship

Knowledge acquired will be assessed via the closed book examination.

Further assessment will be centred around project work. Learners will be assessed on four aspects including; problem statement and understanding, demonstration of practical skills, critical analysis of results, and report writing.

General Notes

N/A