PhD Studentship: Enhancing Maintenance Strategies Through Reliability-Centred Condition Monitoring

Supervised by Rasa Remenyte-Prescott, Rundong (Derek) Yan, and Darren Prescott (Resilience Engineering, Faculty of Engineering) 

Introduction

Modern maintenance strategies rely heavily on condition monitoring to predict failures and improve system performance. However, a significant challenge lies in the vast amounts of data required for effective monitoring. This data-driven approach can result in high costs, increased computational demands, and logistical challenges in data storage and processing. A crucial aspect of addressing these issues is optimising data sampling and condition monitoring strategies. By collecting only the most relevant data at appropriate intervals and tailoring condition monitoring strategies to critical subsystems or components, it is possible to achieve the same level of diagnostic accuracy and asset management effectiveness while significantly reducing the burden of data acquisition and management.

Proposed project

This research, inspired by the philosophy of Reliability-Centred Maintenance (RCM), aims to design innovative frameworks for condition monitoring that optimise data sampling and facilitate the digitalisation of current and future systems. Rooted in the principles of reliability and efficiency, the project will prioritise the criticality of components and their probability of failure to establish appropriate condition monitoring strategies and adaptive sampling techniques that achieve a harmonious balance between efficiency and reliability.

The research seeks to redefine how condition-monitoring systems operate. The proposed approach will reduce unnecessary data collection and help decision-makers and system designers identify the most effective condition-monitoring strategies, enabling industries to adopt maintenance strategies that are both resource-efficient and sustainable. These advancements will contribute to improved operational performance and long-term sustainability in diverse industrial contexts, with applications spanning sectors such as wind turbines and rail tracks.

Summary: Open to UK/EU/Overseas students. Look for funding sources at https://www.nottingham.ac.uk/pgstudy/funding/postgraduate-funding.aspx 

Entry Requirements: We require an enthusiastic graduate with a 1st class degree in engineering, computer science, maths, or a relevant discipline, at integrated Master’s level or with a relevant MSc (in exceptional circumstances a 2:1 degree can be considered). 

To apply click the 'Apply' button above. 

For any enquiries about the project and the funding please email Dr Rundong (Derek) Yan (rundong.yan@nottingham.ac.uk) 

This studentship is open until filled. Early application is strongly encouraged.

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