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An Integrative Computational Method for Gearbox Diagnosis
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
2012 (English)In: EIGHTH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING, Elsevier, 2012, Vol. 12, p. 133-138Conference 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, 2012. Vol. 12, p. 133-138
Series
Procedia CIRP
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-205956DOI: 10.1016/j.procir.2013.09.024ISI: 000396450000023OAI: oai:DiVA.org:kth-205956DiVA, id: diva2:1090873
Conference
Procedia CIRP of the 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering
Note

QC 20170519

Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2017-05-19Bibliographically approved

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