Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis
Joint Authors
Castellanos-Dominguez, German
Holguín-Londoño, Mauricio
Cardona-Morales, Oscar
Sierra-Alonso, Edgar F.
Mejia-Henao, Juan D.
Orozco-Gutiérrez, Álvaro
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-26
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages.
Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM).
This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals.
Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering.
Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal.
Finally, a classification scheme is carried out to distinguish the different types of faults.
The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults.
The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable.
As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.
American Psychological Association (APA)
Holguín-Londoño, Mauricio& Cardona-Morales, Oscar& Sierra-Alonso, Edgar F.& Mejia-Henao, Juan D.& Orozco-Gutiérrez, Álvaro& Castellanos-Dominguez, German. 2016. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112624
Modern Language Association (MLA)
Holguín-Londoño, Mauricio…[et al.]. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis. Mathematical Problems in Engineering No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1112624
American Medical Association (AMA)
Holguín-Londoño, Mauricio& Cardona-Morales, Oscar& Sierra-Alonso, Edgar F.& Mejia-Henao, Juan D.& Orozco-Gutiérrez, Álvaro& Castellanos-Dominguez, German. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112624
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112624