Application of machine learning in condition monitoring

About this project

Condition monitoring of industrial machinery is gaining importance due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. In this work we adapt an efficient machine learning based approach to detect faults of an industrial oil pump using wavelet transform and genetic algorithm. This work has been done in collaboration with a local oil refinery.

Lead researcher

Dr Saeed Shiry Ghidary


Saeed has worked extensively with different industries for office and industrial automation. He has developed domain specific robots for sewer pipe inspection, education, entertainment, rescue, demining, rehabilitation, farming and metal industries.

Saeed's profile
UK University

StudentCrowd University Awards 2022

for Job Prospects

StudentCrowd University Awards 2022

for Student Satisfaction

Complete University Guide 2022

for Social Inclusion

The Times and The Sunday Times Good University Guide 2023

for Course Content

StudentCrowd University Awards 2022

of Research Impact is ‘Outstanding’ or ‘Very Considerable’

Research Excellence Framework 2021