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An EEMD and ICA-based Integrative Approach to Wind Turbine Gearbox Diagnosis
University of Connecticut, Storrs, CT, USA.
University of Connecticut, Storrs, CT, USA.
Southeast University, Nanjing, China.
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.ORCID iD: 0000-0001-8679-8049
2013 (English)In: Eighth CIRP Conference on Intelligent Computation in Manufacturing Engineering / [ed] Roberto Teti, Elsevier, 2013, Vol. 12, 133-138 p.Conference paper, Published paper (Refereed)
Abstract [en]

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 12, 133-138 p.
Series
Procedia CIRP, ISSN 2212-8271 ; 12
Keyword [en]
Wind turbine, bearing diagnosis, EEMD, ICA
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-6856DOI: 10.1016/j.procir.2013.09.024Scopus ID: 2-s2.0-84886785490OAI: oai:DiVA.org:his-6856DiVA: diva2:572376
Conference
CIRP ICME'12 - 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Innovative and Cognitive Production Technology and Systems, 18-20 July 2012, Ischia, Gulf of Naples, Italy
Available from: 2012-11-27 Created: 2012-11-27 Last updated: 2016-06-22Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
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  • vancouver
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