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Dive into the research topics where Ibrahim Rehab is active.

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Featured researches published by Ibrahim Rehab.


international conference on automation and computing | 2015

Diagnosis of combination faults in a planetary gearbox using a modulation signal bispectrum based sideband estimator

Xiange Tian; Gaballa M. Abdallaa; Ibrahim Rehab; Fengshou Gu; Andrew Ball; Tie Wang

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme.


international conference on automation and computing | 2015

A study of diagnostic signatures of a deep groove ball bearing based on a nonlinear dynamic model

Ibrahim Rehab; Xiange Tian; Fengshou Gu; Andrew Ball

For accurate fault detection and diagnosis, this paper focuses on the study of bearing vibration responses under increasing radial clearances due to investable wear and different bearing grades. A nonlinear dynamic model incorporating with local defects and clearance increments is developed for a deep groove ball bearing. The model treats the inner race-shaft and outer race-housing as two lumped masses which are coupled by a nonlinear spring formalized by the Hertzian contact deformation between the balls and races. The solution of the nonlinear equation is obtained by a Runge-Kutta method in Matlab. The results show that the vibrations at fault characteristic frequencies exhibit significant changes with increasing clearances. However, an increased vibration is found for the outer race fault whereas a decreased vibration is found for inner race fault. Therefore, it is necessary to take into account these changes in determining the size of faults.


Mechanical Systems and Signal Processing | 2018

A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram

Xiange Tian; James Xi Gu; Ibrahim Rehab; Gaballa Abdalla; Fengshou Gu; Andrew Ball


Archive | 2014

The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum

Ibrahim Rehab; Xiange Tian; Fengshou Gu; Andrew Ball


International Journal of Hydromechatronics | 2018

The influence of rolling bearing clearances on diagnostic signatures based on a numerical simulation and experimental evaluation

Ibrahim Rehab; Xiange Tian; Fengshou Gu; Andrew Ball


Archive | 2015

A robust fault detection method of rolling bearings using modulation signal bispectrum analysis

Xiange Tian; Fengshou Gu; Ibrahim Rehab; Gaballa Abdalla; Andrew Ball


Archive | 2016

A study of two bispectral features from envelope signals for bearing fault diagnosis

Ibrahim Rehab; Xiange Tiana; Niaoqing Hu; Tianxiao Yan; Ruiliang Zhang; Fengshou Gu; Andrew Ball


Archive | 2017

Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification

Yuandong Xu; Li Haiyang; Dong Zhen; Ibrahim Rehab; Fengshou Gu; Andrew Ball


International Journal of COMADEM | 2016

A Study of the Diagnostic Amplitude of Rolling Bearing under increasing Radial Clearance using Modulation Signal Bispectrum

Ibrahim Rehab; Xiange Tian; Ruiliang Zhang; Fengshou Gu; Andrew Ball


Archive | 2014

Fault severity diagnosis of rolling element bearings based on kurtogram and envelope analysis

Fengshou Gu; Xiange Tian; Zhi Chen; Tie Wang; Ibrahim Rehab; Andrew Ball

Collaboration


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Andrew Ball

University of Huddersfield

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Fengshou Gu

University of Huddersfield

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Xiange Tian

University of Huddersfield

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Gaballa Abdalla

University of Huddersfield

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Ruiliang Zhang

Taiyuan University of Technology

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Tie Wang

Taiyuan University of Technology

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Dong Zhen

University of Huddersfield

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James Xi Gu

Manchester Metropolitan University

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Yuandong Xu

University of Huddersfield

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