Fault Detection in Spindles using Wavelets - State of the Art

C.V. Garzón, G.B. Moncayo, D.H. Alcantara, R. Morales-Menendez

Research output: Contribution to journalArticle

Abstract

The diagnosis and prevention of failures have allowed to evolve the maintenance strategies in the industries, improving the efficiency and optimizing the production stops. In the case of machining systems, timely fault diagnosis avoids products out of specification and/or extreme machines damage. Optimum machining depends of several parameters, including the spindle performance, within which the bearings system represents the mechanical component with the greatest likelihood of failure. From an exhaustive bibliographic review, the advances in the use of the Wavelet Transform (WT) for the analysis of mechanical vibrations of spindle bearings are presented. A fault detection method is proposed, which automatically detects the frequency range where most information of the faults are located and separates them from other frequencies associated with noise. Early results validated with experimental data are promising.
Original languageEnglish
Pages (from-to)450
Number of pages455
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume51
Issue number1
DOIs
Publication statusPublished - 2018
Externally publishedYes

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Bearings (structural)
Fault detection
Machining
Wavelet transforms
Failure analysis
Specifications
Industry

Cite this

Garzón, C.V. ; Moncayo, G.B. ; Alcantara, D.H. ; Morales-Menendez, R. / Fault Detection in Spindles using Wavelets - State of the Art . In: IFAC Proceedings Volumes (IFAC-PapersOnline). 2018 ; Vol. 51, No. 1. pp. 450.
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Fault Detection in Spindles using Wavelets - State of the Art . / Garzón, C.V.; Moncayo, G.B.; Alcantara, D.H.; Morales-Menendez, R.

In: IFAC Proceedings Volumes (IFAC-PapersOnline), Vol. 51, No. 1, 2018, p. 450.

Research output: Contribution to journalArticle

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