DocumentCode :
3411054
Title :
Envelope analysis and data-driven approaches to acoustic feature extraction for predicting the remaining useful life of rotating machinery
Author :
Kavanagh, Darren F. ; Scanlon, Patricia ; Boland, Frank
Author_Institution :
Bell Labs. Ireland, Alcatel Lucent, Dublin
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
1621
Lastpage :
1624
Abstract :
The ability to predict the Remaining Useful Life (RUL) of Rotating Machines is a highly desirable function of Automated Condition Monitoring (ACM) systems. Typically, vibration signals are acquired through contact with the machine and used for monitoring. In this paper, a novel implementation of the ubiquitous feature extraction approach Envelope Analysis (EA) is applied to acoustic noise signals (< 25 kHz) to predict the RUL of a rotating machine. A well known drawback of the EA approach is that the frequency band of interest must be known or pre-estimated. Therefore, this approach is compared to a Data-Driven approach to feature extraction which utilizes an Information Theoretic approach to feature selection that does not require any a-priori information regarding the frequency band of interest. It is shown that the Data- Driven approach, with an accuracy of 97.7%, significantly outperforms the EA approach, with an accuracy of 93.7%. This study also shows that the improved performance of the Data-Driven approach is due to new information being uncovered in spectral locations across the entire spectrum from 0 to 25 kHz, and not just within one frequency band typically used by the EA approach.
Keywords :
acoustic signal processing; condition monitoring; machinery; remaining life assessment; vibrations; acoustic feature extraction; acoustic noise signal; automated condition monitoring system; envelope analysis; information theoretic approach; remaining useful life; rotating machinery; ubiquitous feature extraction approach; vibration signal; Acoustic noise; Acoustic signal detection; Condition monitoring; Data analysis; Feature extraction; Frequency; Machinery; Rotating machines; Signal analysis; Vibrations; Acoustic Signals; Information Theory; Mechanical Bearings; Pattern classification; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4517936
Filename :
4517936
Link To Document :
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