Feature models and condition visualization for rotating machinery fault diagnosis

Author

Rauber, Thomas W. ; de Assis Boldt, Francisco ; Varejao, Flavio M. ; Pellegrini Ribeiro, Marcos

Author_Institution

Dept. de Inf., Univ. Fed. do Espirito Santo, Vitoria, Brazil

fYear

2013

fDate

8-11 Dec. 2013

Firstpage

265

Lastpage

268

Abstract

We discuss appropriate feature models for the automatic diagnosis of faults in two different application scenarios in a comparative study. The first test case is the Case Western Reserve University Bearing Data, the second is a submersible pump used in offshore oil exploration. Additionally we provide a visual comparison of the discriminative capabilities of the employed feature models using Principal Component Analysis and the Sammon plot to show the machine condition patterns.

Keywords

condition monitoring; fault diagnosis; principal component analysis; pumps; turbomachinery; Case Western Reserve University Bearing Data; Sammon plot; condition visualization; fault diagnosis; machine condition patterns; offshore oil exploration; principal component analysis; rotating machinery; submersible pumps; Data models; Data visualization; Frequency-domain analysis; Principal component analysis; Pumps; Support vector machines; Wavelet packets; Fault diagnosis; feature models; high-dimensional pattern visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on

Conference_Location

Abu Dhabi

Type

conf

DOI

10.1109/ICECS.2013.6815405

Filename

6815405