Learning multi-dimensional functions: gas turbine engine modeling

AuthorSearch for:
TypeArticle
Proceedings titleKnowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings
Series titleLecture Notes in Computer Science; no. 4702
ConferenceEuropean Conference on Principles of Data Mining and Knowledge Discovery, Databases, September 17-21, 2007, Warsaw, Poland
ISBN9783540749752
Pages406413
AbstractThis paper shows how multi-dimensional functions, describing the operation of complex equipment, can be learned. The functions are points in a shape space, each produced by morphing a prototypical function located at its origin. The prototypical function and the space’s dimensions, which define morphological operations, are learned from a set of existing functions. New ones are generated by averaging the coordinates of similar functions and using these to morph the prototype appropriately. This paper discusses applying this approach to learning new functions for components of gas turbine engines. Experiments on a set of compressor maps, multi-dimensional functions relating the performance parameters of a compressor, show that it more accurately transforms old maps, into new ones, than existing methods.
Publication date
PublisherSpringer
Linkhttps://link.springer.com/chapter/10.1007/978-3-540-74976-9_40
LanguageEnglish
AffiliationNational Research Council Canada; NRC Institute for Information Technology
Peer reviewedYes
NPARC number23002094
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Record identifier2df6f83d-4db3-408f-9218-563f6251dd59
Record created2017-08-14
Record modified2017-08-14
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