The Through-life Engineering Services Institute hosts 25-30 research students who work on real problems in collaboration with industry. Current research projects, across the full range of the Institute’s interests, include in-depth technical investigations and validations, business applications, and human factors in systems, for example:
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Sensing techniques for manufacturing process degradation, including thermal emissions, vibration and acoustics;
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Signal processing for extracting information from data, analytics and visualisation;
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Repair technologies for a range of metal and composites;
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Management of cost prediction, obsolescence, and solutions for legacy equipment;
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Augmented and virtual reality.
Many of our PhDs have full or part industry funding, and we also research a wide range of topics proposed directly by applicants.
Alternatively, Cranfield offers you the opportunity to study with us on a self-funded basis in the following research areas:
Business modelling to evaluate predictive maintenance benefits for commercial aircraft fleet level total ownership cost
Predictive maintenance is promising a step change to commercial aircraft operations. Benefits realisation depends on the business and operations approach of the airlines and leasing companies. This research aims to complete a model to simulate the dynamics of fleet total cost of ownership, part of the Cranfield Digital Aviation initiative.
Lead academic: Dr Ip-Shing Fan
Information modelling to support paperless operations for commercial aircraft through life maintenance and support
Maintenance of commercial aircraft involves a lot of papers, imposed by authorities and commercial stakeholders. This research works with industrial partners to create and validate an ontology model for aircraft through life support, and develop pre-competitive commercial demonstrations as part of the Cranfield Digital Aviation initiative.
Lead academic: Dr Ip-Shing Fan
Corrosion-sensitive multiscale fatigue modelling
SWAG合集 the effects of corrosion-induced changes in composition on fatigue damage in metallic materials. We will employ multiscale models to understand the role of compositional changes on plastic deformation and inform fatigue prognosis approaches.
Lead academic: Dr Gustavo M. Castelluccio
Multiscale modelling of metallic materials after overloads
SWAG合集 the mechanical response of metals after overloads by developing physics-based models informed with experiments across scales. We seek to explain why some overloads may have no noticeable effect in metals while other events can profoundly affect their mechanical response.
Lead academic: Dr Gustavo M. Castelluccio
Sensor/feature engineering for integrated vehicle health management (IVHM)
IVHM is an enabling discipline consisting of technologies to assess the reliability and failures of a product in its actual life cycle conditions to mitigate system risk. Sensor systems and feature identification are important aspects of an IVHM system design. Current sensor/feature engineering is mainly dependent on past failures and engineering judgements. This PhD will develop a systematic process using energy principles to identify optimum sensor(s)/feature(s) for complex engineering system failures for aerospace, automotive, rail or energy applications.
Lead academic: Dr Suresh Perinpanayagam
Advanced IVHM/data analytics for optimising manufacturing systems
Due to the pressures of modern business constraints, machine downtime can prove to be very costly for businesses, not only in terms of immediate delivery misses, but even more so in terms of long-term loss of customer satisfaction and reputation. For these reasons, having zero unplanned downtime will not only be critical, but also a necessity in the future. Integrated vehicle health management (IVHM) capability can be a critical part of this journey because, by its very nature, IVHM helps detect anomalies and anticipate problems which can be used to schedule inspections, repairs, and overhauls at the most optimal times. This PhD will develop advanced IVHM/data analytics techniques for monitoring and optimising manufacturing systems.
Lead academic: Dr Suresh Perinpanayagam
Novel end-of-life prediction algorithms for batteries
Batteries are widely used in spacecraft, aircraft, and electric vehicles. Hence, an accurate prediction of the remaining useful life (RUL) is essential for these applications. Significant research has been conducted in order to get higher energy density, reduced weight and cost, longer life, or shorter recharge times. New materials and technologies are continually being explored with the goals of improving the technology and satisfying consumer requirements. The ability to precisely monitor and manage battery health will increase performance and avoid failures, such as loss of operation, reduced competency, stoppage, and even catastrophic failure. This PhD will focus on accurately predicting the end-of-life of batteries with different chemistries using model-based deep learning techniques.
Lead academic: Dr Suresh Perinpanayagam
Intelligent systems employing artificial intelligence and machine learning for complex engineering
Intelligent systems are seen as central to delivering intelligent services in business, society and private life. This can be seen from intelligent factory controls, autonomous and connected vehicles, smart energy generators, predictive medicine etc. This PhD programme will prepare you for complex engineering problems in the the aerospace, automotive, rail, power and energy and healthcare sectors.
Lead academic: Dr Suresh Perinpanayagam
Multi-imaging sensors based automatic large parts inspection
Current inspection methods for large parts are either contact or limited to surface detection. This project will develop the proof-of-concept for a scanner-camera-thermography based 3D inspection system that performs a complete inspection for surface and subsurface discontinuities occurring in large industrial parts in an automatic, non-destructive, intelligent and robust manner.
Lead academic: Dr Yifan Zhao
Deep learning based super resolution for non-destructive testing
Develop a deep learning based method to enhance the resolutions of images. High spatial and temporal resolution images are strongly demanded in various non-destructive testing. In many cases it is difficult to obtain them due to high cost and inherent physical constraints of sensors or bandwidth.
Lead academic: Dr Yifan Zhao
