Maintaining model efficiency, avoiding bias and reducing input parameter volume in compressor fault classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

With the exponential growth in data collection and storage and the necessity for timely prognostic health monitoring of industrial processes traditional methods of data analysis are becoming redundant. Big data sets and huge volumes of inputs give rise to equally massive computational requirements. In this paper the differences in input parameter selection using a subset of the original variables and using data reduction techniques are compared. Each selection procedure being illustrated by both statistical methods and machine learning techniques. It is shown that the subsequent classification models are highly comparable. Finally the merits of a combined multivariate statistical and wavelet decomposition approach is considered. Techniques are applied to output signals from an experimental compressor rig.

Original languageEnglish
Title of host publicationProceedings of 2016 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages196-201
Number of pages6
ISBN (Electronic)9781467388290
DOIs
Publication statusPublished - 23 Aug 2016
Event7th International Conference on Mechanical and Aerospace Engineering - London, United Kingdom
Duration: 18 Jul 201620 Jul 2016
Conference number: 7

Conference

Conference7th International Conference on Mechanical and Aerospace Engineering
Abbreviated titleICMAE 2016
CountryUnited Kingdom
CityLondon
Period18/07/1620/07/16

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