About the project
Health and Usage Monitoring System (HUMS) refers to systems that have been developed to help ensure machine availability, reliability and safety using data collection and analysis techniques.
It is particularly used for rotorcraft in aerospace but has also been introduced to offshore oil industry since the Chinook crash in the North Sea in 1986. Despite the high demand and successful applications, current HUMS technology focuses only on safety functions.
HUMS has the potential to significantly improve performance, operation, maintenance and cost-benefit efficiency for machinery and it is also being advanced into machine remaining useful life (RUL) prediction and condition-based maintenance (CBM), taking the advantage of fast development of artificial intelligence (AI) and machine learning (ML) algorithms and advanced modelling techniques.
This project aims to develop a new generation HUMS with gearbox digital twins, generalised ML models for bearing and gear fault detection and diagnosis, and robust models for their RUL prediction. Sensor data created from this project as well as databases in literature will be utilised in the model development.
This project has a broad scope of research. You may focus on bearing/gear health monitoring, gearbox RUL modelling, or on the development of gearbox digital twins.
Applicants with a passion in developing AI and ML based techniques are encouraged to get in touch to discuss more details and submit an application.
