PhD Studentship: Development of a New Generation Health and Usage Monitoring System (HUMS) based on Machine Learning and Digital Twin Technologies

Supervisory Team: Ling Wang

Project description:

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. Successful candidates are expected to have strong experience in mechanical engineering (especially in materials, rotating machines, mechanical testing and analysis), sensors and signal processing technologies, and sufficient understanding on AI and ML methods and their applications in mechanical systems. Matlab and computing programme skills are essential to this research.

Entry Requirements

A good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships.  For more information please visit PhD Scholarships | Doctoral College | University of Southampton  Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online:  Search for a Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Ling Wang

Applications should include:

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

Email: feps-pgr-apply@soton.ac.uk

The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward. 

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