Fault detection via sparsity-based blind filtering on experimental vibration signals

Research output: Chapter in Book/Report/Conference proceedingConference paper

4 Downloads (Pure)

Abstract

Detection of bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of the extensive number of machine components. One recent method proposed in literature addresses these difficulties by aiming to increase the sparsity of the envelope spectrum of the vibration signal via blind filtering (Peeters. et al., 2020). As the name indicates, this method requires no prior knowledge about the machine. Sparsity measures like Hoyer index, l1/l2 norm, and spectral negentropy are optimized in the blind filtering approach using Generalized Rayleigh quotient iteration. Even though the proposed method has demonstrated a promising performance, it has only been applied to vibration signals of an academic experimental test rig. This paper focuses on the real-world performance of the sparsity-based blind filtering approach on a complex industrial machine. One of the challenges is to ensure the numerical stability and the convergence of the Generalized Rayleigh quotient optimization. Enhancements are thus made by identifying a quasi-optimal filter parameter range within which blind filtering tackles these issues. Finally, filtering is applied to certain frequency ranges in order to prevent the blind filtering optimization from getting skewed by dominant deterministic healthy signal content. The outcome proves that sparsity-based blind filters are effective in tracking bearing faults on real-world rotating machinery without any prior knowledge of characteristic frequencies.
Original languageEnglish
Title of host publicationAnnual Conference of the PHM Society
PublisherPROCEEDINGS OF THE ANNUAL CONFERENCE OF THE PHM SOCIETY 2021
Number of pages10
Volume13
Edition1
Publication statusPublished - Nov 2021
Event13th Annual Conference of the Prognostics and Health Management Society - Remote, United States
Duration: 29 Nov 20212 Dec 2021
https://www.phmsociety2021.com

Conference

Conference13th Annual Conference of the Prognostics and Health Management Society
CountryUnited States
CityRemote
Period29/11/212/12/21
Internet address

Fingerprint

Dive into the research topics of 'Fault detection via sparsity-based blind filtering on experimental vibration signals'. Together they form a unique fingerprint.

Cite this