Rotating machine fault diagnosis using empirical mode decomposition

Gao, Qiang; Duan, Chang‐Kui; Fan, Hong; Meng, Qingfeng

doi:10.1016/j.ymssp.2007.10.003

Cited by 180 publications

(98 citation statements)

References 16 publications

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“…A proposed diagnosis model with the Mahalanobis distance of feature vectors was used to recognize bearing faults in three conditions: normal, outer race and inner race damage. The authors in [11] explained that using the IMFs alone for rotating machine fault detection could not work well with noisy vibration data. They combined some of the consecutive IMFs into one and called it a combined mode functions (CMD) and utilized it for the fault detection of generators.…”

Section: Adaptive Empirical Mode Decomposition For Bearing Fault Detementioning

confidence: 99%

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan

Nguyen

2016

SV-JME

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Section: Adaptive Empirical Mode Decomposition For Bearing Fault Detementioning

confidence: 99%

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan

Nguyen

2016

SV-JME

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“…It decomposes the signal into a number of IMFs, each of which is a mono-component function. The multi-components signal (the current i in our case) is then decomposed into M intrinsic modes and a residue R M [25][26][27].…”

Section: The Emd Brieflymentioning

confidence: 99%

EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component

Amirat

Choqueuse

Benbouzid

2013

Mechanical Systems and Signal Processing

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Failure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems.This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the ensemble empirical mode decomposition as a tool for failure detection in wind turbine generators for stationary and non stationary cases.

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“…The EMD method has recently focused considerable attention and been widely indexed to rotating machinery fault detection [22][23] [24][25].…”

Section: The Emd Brieflymentioning

confidence: 99%