Comparison between Wigner–Ville distribution- and empirical mode decomposition vibration-based techniques for helical gearbox monitoring

Ibrahim, Ghalib R.; Albarbar, Alhussein

doi:10.1177/0954406211403571

Cited by 27 publications

(10 citation statements)

References 20 publications

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“…Moreover, it is considered very expensive in terms of computation time and resources. Despite these drawbacks, EMD and EEMD have been successfully applied for fault diagnosis of various rotating machinery faults [1,6,[25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Enhancement Based on Parameterless Empirical Wavelet Transform: Application to Bearing Fault Diagnosis

El-Mongy

2018

The International Conference on Applied Mechanics and Mechanica

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Rolling-element bearings are usually subject to faults that need prompt detection in order to prevent sudden failures. Many time-frequency analysis techniques have been used for the purpose of bearing fault detection and diagnosis.From these techniques, wavelets and empirical mode decomposition (EMD) stand out as the most widely applied methods in bearing fault diagnosis. Recently, a novel method named the parameterless empirical wavelet transform (PEWT) has been proposed to combine the wavelet formulation with the adaptability of the empirical mode decomposition. In this paper, the parameterless empirical wavelet transform (PEWT) is combined with envelope detection (ED) to present a new scheme named PEWT-ED for non-stationary signal analysis. The capabilities and limitations of the new method in bearing fault diagnosis are investigated using simulation and experiment. The results show that the new approach can effectively extract the bearing fault characteristics. The PEWT-ED is found to be a powerful tool in signal de-noising and enhancement for fault diagnosis purposes.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction Enhancement Based on Parameterless Empirical Wavelet Transform: Application to Bearing Fault Diagnosis

El-Mongy

2018

The International Conference on Applied Mechanics and Mechanica

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“…The divergence of CBM development has created a massive number of research questions and ideas. Many researchers have emphasised in developing novel techniques and analysis to tackle condition monitoring problems (He et al 2009;Son et al 2009;Yun et al 2011;Ibrahim and Albarbar 2011;Li et al 2005). To practically realise the implementation of software algorithms, other researchers have focused in developing system frameworks for specific CBM tasks (Niu, Yang, and Pecht 2010;Trutt, Sottile, and Kohler 2002;Han and Yang 2006).…”

Section: Introductionmentioning

confidence: 99%

Adoption of MEMS technology in e-maintenance systems for rotating machinery

Teay¹,

Albarbar²

2017

insight

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Wireless sensor networks with on-board signal processing capabilities are becoming very attractive for machinery condition monitoring purposes. Their advantages in cost and size are becoming an important factor for deployment in a new generation of maintenance-e-maintenance. In this paper, an intelligent monitoring system for emaintenance (IMSEM) is presented using a recently developed and tested MEMS accelerometer, a low-power microprocessor and a wireless communication module. The system has a compatible framework and interface with Open Systems Architecture for Condition-Based Maintenance (OSA-CBM). Importantly, it integrates OSA-CBMdefined functions, including sensing module, signal processing, condition monitoring and health assessment; thus, the developed system can successfully reduce-the monitoring complexity and communication overhead with human operators. The performance of IMSEM is evaluated by carrying out fault diagnostic on rotating unbalance of a mechanical shaft driven by a DC motor with varying loads and speeds. By analysing the signals from the vibration signal and rotating speed, the IMSEM has the ability to carry out on-board fault detection and diagnosis. The IMSEM achieved an accuracy of 95% in detecting the fault and severity of rotor unbalance, which is based on the ISO 1940-1:2003.

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“…They used several statistical indicators to reflect the change in the vibration response caused by the tooth crack. Ibrahim and Albarbar [4] presented a comparison between empirical mode decomposition (EMD) and smoothed pseudo-Wigner-Ville distribution (SPWVD) methods based on vibration signature and energy calculation procedure for monitoring gearbox systems. This article illustrates that the calculation of energy using EMD techniques offers a more effective way to detect early faults than the SPWVD method, and that the computation of energy using the EMD technique is faster than the SPWVD method.…”

Section: Introductionmentioning

confidence: 99%

Varying Load Detection in A Gearbox System Based on Adaptive Threshold Estimation

Kouadri

Ibrahim

Albarbar

2016

MATEC Web of Conferences

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Abstract. This article presents an adaptive threshold computation for varying load detection in gearbox system. This method was applied on the vibration signal which is captured from gearbox housing. The experimental work was carried out using a three-stage gearbox connected with a motor and a generator. The developed adaptive threshold is obtained through several repeated experiments in the healthy mode and under the same operating conditions. An appropriate statistical test is used to examine the validity of the adaptive threshold estimation approach. Besides and for all experiments, a confidence interval is obtained which is closely linked to the distribution frequencies of the gearbox vibration signal as a random variable. In addition, several significance levels are considered to show the performances of the proposed adaptive thresholding technique compared to the limitations of the fixed threshold through the rate of false detection alarms. It is demonstrated from various experimental varying load mode of a gearbox system the effectiveness and accuracy of the adaptive threshold in term of evaluating gearbox operating conditions.

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Comparison between Wigner–Ville distribution- and empirical mode decomposition vibration-based techniques for helical gearbox monitoring

Cited by 27 publications

References 20 publications

Feature Extraction Enhancement Based on Parameterless Empirical Wavelet Transform: Application to Bearing Fault Diagnosis

Feature Extraction Enhancement Based on Parameterless Empirical Wavelet Transform: Application to Bearing Fault Diagnosis

Adoption of MEMS technology in e-maintenance systems for rotating machinery

Varying Load Detection in A Gearbox System Based on Adaptive Threshold Estimation

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