Multi-fault diagnosis of rotor system based on differential-based empirical mode decomposition

Li, Ming; Li, Fucai; Beibei, Jing; Bai, Huiyu; Li, Hongguang; Meng, Guang

doi:10.1177/1077546313502505

Cited by 31 publications

(20 citation statements)

References 28 publications

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“…6, its empirical mode decomposition (EMD) [24,25] is shown in Fig. 15 with : where γ i (t) is an intrinsic mode function (IMF) and r(t) is a residual monotonic function featuring negligible amplitudes.…”

Section: Frequency Analysis For Gauge 1bmentioning

confidence: 99%

Experimental and numerical simulation of a rotor/stator interaction event localized on a single blade within an industrial high-pressure compressor

Batailly

Agrapart²,

Millecamps³

et al. 2016

Journal of Sound and Vibration

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a b s t r a c tThis contribution addresses a confrontation between the experimental simulation of a rotor/stator interaction case initiated by structural contacts with numerical predictions made with an in-house numerical strategy. Contrary to previous studies carried out within the low-pressure compressor of an aircraft engine, this interaction is found to be nondivergent: high amplitudes of vibration are experimentally observed and numerically predicted over a short period of time. An in-depth analysis of experimental data first allows for a precise characterization of the interaction as a rubbing event involving the first torsional mode of a single blade. Numerical results are in good agreement with experimental observations: the critical angular speed, the wear patterns on the casing as well as the blade dynamics are accurately predicted. Through out the article, the in-house numerical strategy is also confronted to another numerical strategy that may be found in the literature for the simulation of rubbing events: key differences are underlined with respect to the prediction of non-linear interaction phenomena.

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Section: Frequency Analysis For Gauge 1bmentioning

confidence: 99%

Experimental and numerical simulation of a rotor/stator interaction event localized on a single blade within an industrial high-pressure compressor

Batailly

Agrapart²,

Millecamps³

et al. 2016

Journal of Sound and Vibration

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“…With the decomposition transform of SGW, the reconstruction transform of SGW can be implemented easily by inversing (1), (2), and (3).…”

Section: Redundant Second-generation Waveletmentioning

confidence: 99%

“…To illustrate the effectiveness of ARSGW and IDRE on getting the optimal signal denoising result and the denoising result evaluation, respectively, the constructed signal is also denoised by RSGW with different values of , , and . Table 2 shows SNR out and IDRE of the denoised signal with setting to the integer within [1,6] and ( , ) setting to (2, 2), (2, 4), (4,2), and (4, 4), respectively.…”

Section: Hydroturbine Unit Vibration Simulation Signal Denoisingmentioning

confidence: 99%

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Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

Lü

Zhang²,

Cheng

et al. 2016

Mathematical Problems in Engineering

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Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis.

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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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Multi-fault diagnosis of rotor system based on differential-based empirical mode decomposition

Cited by 31 publications

References 28 publications

Experimental and numerical simulation of a rotor/stator interaction event localized on a single blade within an industrial high-pressure compressor

Experimental and numerical simulation of a rotor/stator interaction event localized on a single blade within an industrial high-pressure compressor

Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

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

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