Imperial College London
Alternate

DrAndrewDuncan

Faculty of Natural Sciences, Department of Mathematics

Senior Lecturer in Statistics and Data-Centric Engineering
 
 
 
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Contact

 

a.duncan

 
 
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Location

 

6M14Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Seshadri:2022:10.1017/dce.2022.29,
author = {Seshadri, P and Duncan, A and Thorne, G and Parks, G and Girolami, M and Vazquez, R},
doi = {10.1017/dce.2022.29},
journal = {Data-Centric Engineering},
pages = {e29--1--e29--30},
title = {Bayesian assessments of aeroengine performance with transfer learning},
url = {http://dx.doi.org/10.1017/dce.2022.29},
volume = {3},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Aeroengine performance is determined by temperature and pressure profiles along various axial stations withinan engine. Given limited sensor measurements both along and between axial stations, we require a statisticallyprincipled approach for inferring these profiles. In this paper we detail a Bayesian methodology for interpolatingthe spatial temperature or pressure profile at axial stations within an aeroengine. The profile at any given axialstation is represented as a spatial Gaussian random field on an annulus, with circumferential variations modelledusing a Fourier basis and radial variations modelled with a squared exponential kernel. This Gaussian randomfield is extended to ingest data from multiple axial measurement planes, with the aim of transferring informationacross the planes. To facilitate this type of transfer learning, a novel planar covariance kernel is proposed, withhyperparameters that characterise the correlation between any two measurement planes. In the scenario where frequencies comprising the temperature field are unknown, we utilise a sparsity-promoting prior on the frequencies toencourage sparse representations. This easily extends to cases with multiple engine planes whilst accommodatingfrequency variations between the planes. The main quantity of interest, the spatial area average is readily obtainedin closed form. We term this the Bayesian area average and demonstrate how this metric offers far more representative averages than a sector area average—a widely used area averaging approach. Furthermore, the Bayesian areaaverage naturally decomposes the posterior uncertainty into terms characterising insufficient sampling and sensormeasurement error respectively. This too provides a significant improvement over prior standard deviation baseduncertainty breakdowns.
AU  - Seshadri,P
AU  - Duncan,A
AU  - Thorne,G
AU  - Parks,G
AU  - Girolami,M
AU  - Vazquez,R
DO  - 10.1017/dce.2022.29
EP  - 1
PY  - 2022///
SN  - 2632-6736
SP  - 29
TI  - Bayesian assessments of aeroengine performance with transfer learning
T2  - Data-Centric Engineering
UR  - http://dx.doi.org/10.1017/dce.2022.29
UR  - https://www.cambridge.org/core/journals/data-centric-engineering/article/bayesian-assessments-of-aeroengine-performance-with-transfer-learning/755A738F7124E3CCF6B564EC86AC76AA
UR  - http://hdl.handle.net/10044/1/99314
VL  - 3
ER  -
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