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An effective method for bearing faults diagnosis

Authors: A. Boudiaf; S. Bouhouche; A. K. Moussaoui; T. Samira;

An effective method for bearing faults diagnosis

Abstract

The bearings are the most important mechanical elements of rotating machinery. They are employed to support and rotate the shafts in rotating machinery. An unexpected defect of the bearing may cause significant economic losses. For that reason, the condition monitoring of these bearings has become a fundamental axis of development and industrial research. The focus of this paper is to combine tow conventional methods: Hilbert Transform (HT) and Discrete Wavelet Transform (DWT) to develop a better method for detection and diagnosis the bearing faults. This new method applied on real measurement signals collected from an experimental vibration system. The monitoring results indicate that the proposed method improves the bearing faults diagnosis compared to other common techniques.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Top 10%
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