Predictive Maintenance

SHE level M
SCQF credit points 15.0
ECTS credit points 7.5
Module code MMH326845
Module Leader Amit Kumar Jain
School School of Computing, Engineering and Built Environment
Subject Mechanical Engineering
Trimester A (September start)

Summary of content

Predictive maintenance stems from new opportunities to capitalise on smart digital revolution, and more specifically on advances in decision support tools powered by big data analytics. The wide shift towards smarter industry (Industry 4.0) has revolutionise the maintenance and asset management.
The use of reliability, failure analysis, artificial intelligence, machine learning, big data analytic approaches is forever changing the way data is collected, analysed, and interpreted. Industrial Internet of things plays a crucial role here, using sensors to render actions into signals. The signals that are transmitted digitally where they can determine things like the functionality of equipment. This process is called predictive maintenance and it is fundamentally changing the industry. The module provides the fundamental principles and understanding towards the use of predictive maintenance techniques and its applications in industry.

Module details

Module structure

Activity Total hours
Lectures 12.00
Tutorials 6.00
Practicals 6.00
Independent Learning 110.00
Assessment 16.00

Assessment methods

Component Duration Weighting Threshold Description
Course Work001 50 45 Report - 2,000 words (FT & DL)
Exam002 50 45 Exam (FT & DL)