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Erschienen in:
Advanced Signal Processing for Structural Health Monitoring Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition

verfasst von : Yaguo Lei

Erschienen in: Structural Health Monitoring

Verlag: Springer International Publishing

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Abstract

Rotating machinery covers a broad range of mechanical equipment in industrial applications. It generally operates under tough working environment and is therefore subject to faults easily. Vibration signals collected in the working process have valuable contributions for the presentation of conditions of the rotating machinery. Consequently, using signal processing techniques, these faults could be detected and diagnosed. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been widely applied in fault diagnosis of rotating machinery. This chapter attempts to introduce the recent research and development of EMD in fault diagnosis of rotating machinery, including basic concepts and fundamental theories about EMD methods and improved EMD methods. Moreover, the applications of EMD methods and improved EMD methods in fault diagnosis of common and key components of rotating machinery, like rotors, gears and rolling element bearings, are described in details.

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Vorheriges Kapitel Sparse Representation of the Transients in Mechanical Signals
Nächstes Kapitel Bivariate Empirical Mode Decomposition and Its Applications in Machine Condition Monitoring
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Metadaten
Titel
Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition
verfasst von
Yaguo Lei
Copyright-Jahr
2017
Buch
Structural Health Monitoring

Print ISBN: 978-3-319-56125-7

Electronic ISBN: 978-3-319-56126-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-56126-4_10

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