Wyniki wyszukiwania - BazTech

1

Wasserstein Distance-EEMD Enhanced Multi-Head Graph Attention Network for Rolling Bearing Fault Diagnosis Under Different Working Conditions

Wang Xingbing, Yao Yunfeng, Gao Chen

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 2

art. no. 184037

EN

Traditional fault diagnosis models often overlook the interconnections between segments of vibration data, resulting in the loss of critical feature information. Additionally, the vibration signals of rolling bearings exhibit non-linear behaviors during operation. Therefore, an efficient fault diagnosis model tailored for rolling bearings is proposed in this paper. In the proposed model, the 1D vibration signals are first preprocessed using ensemble empirical mode decomposition (EEMD). This technique generates multiple intrinsic mode functions (IMF) as individual nodes. The percentage distance between each node is calculated using the Wasserstein distance (WD) to capture the relationships between nodes and use it as the edge weights to construct a node graph. This unique approach enhances the transformation of 1D vibration signals into a node graph representation, preserving important information. An improved multi-head graph attention network (MGAT) model is established to extract features and perform classification on the node graph. This MGAT model effectively utilizes the relationships between nodes and enhances the accuracy of fault diagnosis. The experimental results demonstrate that the proposed method achieves higher accuracy compared to similar modelswhile requiring less processing time. The proposed approach contributes significantly to the field of fault diagnosis for rolling bearings and provides a valuable tool for practical applications.

2

Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes

Sun Shuang, Przystupa Krzysztof, Wei Ming, Yu Han, Ye Zhiwei, Kochan Orest

Eksploatacja i Niezawodność

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2020

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Vol. 22, no. 4

730--740

EN

Fault diagnosis is part of the maintenance system, which can reduce maintenance costs, increase productivity, and ensure the reliability of the machine system. In the fault diagnosis system, the analysis and extraction of fault signal characteristics are very important, which directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis method based on the ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive Bayes (NB) is proposed, which combines traditional pattern recognition methods meta-heuristic search can overcome the difficulty of selecting classifier parameters while solving small sample classification under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and methods was also displayed in detail. The results manifest the efficiency and accuracy of signal sparse representation and fault type classification has been enhanced.

3

Pulsation signals analysis of turbocharger turbine blades based on optimal EEMD and TEO

Wang Fengli

Polish Maritime Research

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2019

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nr 3

78--86

EN

Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.

4

New gas-liquid two-phase flow pattern maps based on the energy ratio of pressure fluctuation through a Venturi tube

Sun Zhiqiang, Chen Luyang, Yao Fengyan

Metrology and Measurement Systems

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2019

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Vol. 26, nr 2

241--252

EN

To find effective and practical methods to distinguish gas-liquid two-phase flow patterns, new flow pattern maps are established using the differential pressure through a classical Venturi tube. The differential pressure signal was first decomposed adaptively into a series of intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition. Hilbert marginal spectra of the IMFs showed that the flow patterns are related to the amplitude of the pressure fluctuation. The cross-correlation method was employed to sift the characteristic IMF, and then the energy ratio of the characteristic IMF to the raw signal was proposed to construct flow pattern maps with the volumetric void fraction and with the two-phase Reynolds number, respectively. The identification rates of these two maps are verified to be 91.18% and 92.65%. This approach provides a cost-effective solution to the difficult problem of identifying gas-liquid flow patterns in the industrial field.

5

Investigation of supra-harmonics through signal processing methods in smart grids

Yalcin T., Ozdemir M., Kostyla P., Leonowicz Z.

Transactions on Environment and Electrical Engineering

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2017

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Vol. 2, No. 2

80--85

EN

Nowadays supra-harmonic distortion studies are gaining attention day by day in power quality research area. When handling communication systems especially Power Line Carrier (PLC) systems in frequency range 2-150 kHz, they are suitable for causing electromagnetic interference (EMI) to other systems. This study shows results of analysis employing advanced method called ensemble empirical mode decomposition (EEMD) to describe supra-harmonic distortion. Unlike the traditional method (short time fourier transform-STFT), EEMD gives extensive representation for supra-harmonic components.

6

Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination

Zheng J.

Archives of Civil and Mechanical Engineering

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2016

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Vol. 16, no. 4

784--794

EN

An automatic fault diagnosis method for rolling bearing is proposed in this paper. Partially ensemble empirical mode decomposition (PEEMD) is developed to solve the problem of mode mixing existing in empirical mode decomposition. Compared with the ensemble empirical mode decomposition, PEEMD generates much more accurate intrinsic mode functions (IMFs) and the decomposing results are complete and orthogonal. Therefore, PEEMD is utilized to preprocess the vibration signals of rolling bearing. Moreover, the features in time, frequency domains of IMFs and ones of original data in time–frequency domain are extracted to reflect the change of fault information. To avoid the high dimension of features, Laplacian score for feature selection is utilized to sort the initial features according to their significances. The pattern recognition method, variable predictive model-based class discrimination (VPMCD) is introduced to achieve an automatic fault diagnosis. Finally, the proposed fault diagnosis method for rolling bearing is applied to analyze the experimental data and the result indicates that the proposed method can effectively diagnose the fault categories and severities of rolling bearings.