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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 7906834, 14 pages
http://dx.doi.org/10.1155/2016/7906834
Research Article

Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

1Automatic Research Group, Universidad Tecnológica de Pereira, Pereira, Colombia
2Technological and Environmental Advances Research Group, Universidad Católica de Manizales, Colombia
3Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
4Universidad Nacional de Colombia, Manizales, Colombia

Received 19 February 2016; Revised 10 May 2016; Accepted 8 June 2016

Academic Editor: Weihua Li

Copyright © 2016 Mauricio Holguín-Londoño et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.