Areas of expertise
- Computing, Simulation & Modelling
- Manufacturing Systems
- Throughlife Engineering Services
Alex graduated from Kingston University with a First-Class BSc Hons degree in Aerospace Engineering. While studying at Kingston, Alex was active member of the kayak club and was elected president in his final year. He moved on to complete an MSc in Aerospace Manufacturing at Cranfield University. He chose the MSc to further advance his knowledge in the design and manufacture of aerospace materials, components and systems. The course included a three-month group project in partnership with TfL to develop a maintenance practice assessment process to enable accurate and informative assessment of maturity levels through a score-based system. This was followed by a four-month individual research project and thesis in partnership with BAE Systems, which focused on the mathematical quantification and interpretation of uncertainties relevant for industrial maintenance. Alex has extended his research in this area through the pursuit of a PhD in advanced uncertainty quantification techniques for maintenance planning.
Alex is currently pursuing a PhD in advanced uncertainty quantification techniques for maintenance planning.
Modern complex engineering systems (CES) are expected to function effectively whilst maintaining reliability in service. This presents significant challenges to confidently and accurately predict maintenance costs and asset availability. These challenges raise varying degrees of uncertainty stemming from multiple heuristic and statistical sources throughout the in-service life of CES. Techniques to quantify uncertainty from statistical sources are well documented, but methods to obtain and analyse heuristic attributes often go undefined and unmitigated, which raise further uncertainties.
A holistic view is necessary to improve decision-making capabilities and reduce maintenance costs and turnaround time. This project aims to develop an intelligent uncertainty quantification system that learns from a combination of historic equipment data and heuristic estimates to allow the user to forecast the level of uncertainty through the in-service phase of CES.
This work builds on projects undertaken in conjunction with the Through-Life Engineering Services (TES) centre at Cranfield University that have been applied in industry to tackle cost uncertainty at the bidding stage, which face broadly similar challenges as those in service. Core factors that influence uncertainty and hinder confident forecasting include quality of available data, experience and knowledge. These have been examined through collaboration with experts from BAE Systems, along with current practice in uncertainty assessment and future maritime support programmes to address challenges. Accurate forecasts in service depend on reliable data and predictable maintainer performance levels. A holistic view ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle.
Articles In Journals
- Grenyer A, Erkoyuncu JA, Zhao Y & Roy R (2021) A systematic review of multivariate uncertainty quantification for engineering systems, CIRP Journal of Manufacturing Science and Technology, 33 (May) 188-208.
- Grenyer A, Schwabe O, Erkoyuncu JA & Zhao Y (2021) Dynamic multistep uncertainty prediction in spatial geometry, Procedia CIRP, 96 74-79. Dataset/s: 10.17862/cranfield.rd.12906716
- Grenyer A, Dinmohammadi F, Erkoyuncu JA, Zhao Y & Roy R (2020) Current practice and challenges towards handling uncertainty for effective outcomes in maintenance, Procedia CIRP, 86 282-287.
- Grenyer A, Addepalli S, Zhao Y, Oakey L, Erkoyuncu JA & Roy R (2018) Identifying challenges in quantifying uncertainty: case study in infrared thermography, Procedia CIRP, 73 108-113.
- Farsi M, Grenyer A, Sachidananda M, Sceral M, Mcvey S, Erkoyuncua J & Roy R (2018) Conceptualising the impact of information asymmetry on through-life cost: case study of machine tools sector, Procedia Manufacturing, 16 99-106.
- Grenyer A, Erkoyuncu JA, Addepalli S & Zhao Y (2020) An uncertainty quantification and aggregation framework for system performance assessment in industrial maintenance. In: TESConf 2020 - 9th International Conference on Through-life Engineering Services, Online, 3-4 November 2020. Dataset/s: 10.17862/cranfield.rd.12906443