Diagnosis of mechanical defects using a hybrid method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and optimized wavelet multi-resolution analysis (OWMRA): experimental study

Babouri, Mohamed Khemissi; Ouelaa, Nouredine; Kebabsa, Tarek; Djebala, Abderrazek

doi:10.1007/s00170-020-06496-z

Cited by 13 publications

(11 citation statements)

References 32 publications

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“…After obtaining each order of IMF components, white noise was added to the residual value so as to obtain the mean IMF and take successive iterations until complete signal decomposition occurred. CEEMDAN could not only effectively address the mode aliasing problem in EMD [19] but also overcome the transfer of white noise from high to low frequency.…”

Section: Ceemdan Algorithmmentioning

confidence: 99%

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Research on Wheel-Rail Local Impact Identification Based on Axle Box Acceleration

Zeng

Sun

2022

Shock and Vibration

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Abnormal vibration signals of tramcar are mostly nonstationary and nonlinear signals. This study applied the Hilbert–Huang transform (HHT) to analyze abnormal vibration of the tramcar, aiming to overcome the limitations of some traditional time-frequency analysis methods, such as Fourier transform, in dealing with nonstationary and nonlinear signals. Additionally, to address mode aliasing produced during empirical mode decomposition (EMD) used in classical HHT, this study proposed to first use complete EMD with adaptive noise for the decomposition of original vibration data, then eliminate the trend-term components with the calculated correlation coefficients, and finally perform denoising on high-frequency noisy components using the wavelet threshold method. After weighted reconstruction using denoised high-frequency components and low-frequency information components, data processing was finally optimized via HHT. Taking a tramcar as an example, the Hilbert spectra of the vertical acceleration of axle box were plotted via HHT, and the time-instantaneous, frequency-instantaneous energy 3D relations were obtained for the location of impact points. Further, the vibration characteristics were analyzed and quality indexes were calculated in combination with the marginal spectra so as to judge the reasons for abnormal vibration and failure modes of the tramcar. The results revealed that the proposed method was feasible and effective in vibration feature extraction and vibration impact analysis for tramcars.

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Section: Ceemdan Algorithmmentioning

confidence: 99%

“…Chen et al proposed particle swarm optimization least squares support vector machine [18] . Babouri et al proposed a hybrid method based on adaptive noise fully integrated empirical mode decomposition, optimized wavelet multiresolution analysis, and Hilbert transform [19].…”

Section: Introductionmentioning

confidence: 99%

Research on Wheel-Rail Local Impact Identification Based on Axle Box Acceleration

Zeng

Sun

2022

Shock and Vibration

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“…Moumene et al [11] propose an improved gear and bearing fault diagnosis method, using an optimized wavelet packet transform (OWPT) for signal denoising and fault feature extraction. Barbour et al [12] propose a hybrid method based on fully ensemble empirical mode decomposition, with adaptive noise, optimized wavelet multiresolution analysis, and Hilbert transforms, which has practical significance for detecting potential mechanical faults. Lior et al [13] conduct experimental simulations on healthy gears and faulty gears through studying the influence of surface roughness and the influence of different working conditions and gear faults on gear vibration.…”

Section: Introductionmentioning

confidence: 99%

Study on Wear Mechanism of Helical Gear by Three-Body Abrasive Based on Impact Load

Wang

Guo

et al. 2022

Materials

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This study aimed to explore the wear characteristics and evolution mechanisms of large-scale wind power gears under the impact load of particles of the three-body abrasive Al2O3 (0.2 mg/mL) from four aspects: oil analysis, vibration analysis, amount of gear wear, and tooth-surface-wear profile analysis. A magnetic powder brake was used to simulate the actual working conditions. Combined with the abrasive particle monitoring and the morphology analysis of the tooth-surface-wear scar, by setting quantitative hard particles in the lubricating oil, the gears are mainly operated in the abrasive wear state, and wear monitoring and wear degree analysis are carried out for the whole life cycle of the gears. Oil samples were observed and qualitatively analyzed using a particle counter, a single ferrograph, a metallographic microscope, and a scanning electron microscope. The experiments demonstrate that the initial hard particles have a greater impact in the early wear stage of the gears (<20 h), and abrasive particle concentration increases by 30%. This means that Al2O3 particles accelerate the gear wear during the running-in period. The loading method of the impact load on the oblique gear exacerbates the abrasion particle wear and expands the stress concentration, which reduces the surface of large milling particles on the surface, and reduces the width of the tooth (the part above the pitch line is severely worn), which causes the gear to break into failure. The research provides help for analyzing the mechanism of abrasive wear of gears and predicting wear life.

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“…Compared with traditional time-frequency domain and wavelet packet analyses, the adaptive decomposition method is more intuitive and adaptive given that no basis function needs to be preset. Accordingly, this method has attracted wide usage used in the field of fault diagnosis [8][9][10]. Empirical mode decomposition (EMD) is a recursive mode decomposition method that uses the extreme points of the original signal to perform multiple envelope calculations.…”

Section: Introductionmentioning

confidence: 99%

Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

Zhao

Zhang

et al. 2021

IEEE Access

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To solve the problems related to the complex structures, multiple parts, and imperceptible assembly quality of combines, this paper compares the performance of the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and information entropy features in the detection data of combine assembly quality, and proposes a vibration detection method of combine assembly quality detection based on multi-entropy feature fusion and optimized least squares support vector machine. Firstly, the vibration signals of the combine are decomposed by various adaptive algorithms, and intrinsic modal function (IMF) components are obtained. Three entropy features are extracted from the components of the modes. The features are visualized by t-distributed stochastic neighbor embedding (t-SNE), and the performance of these entropy features in the combine assembly quality detection is analyzed. Secondly, a feature extraction method based on information entropy fusion is proposed. The optimized kernel principal component analysis (KPCA) is used to fuse and reduce the dimension of the entropy features, and form the fusion features. Finally, the extracted features are imported into optimization least squares support vector machine (LSSVM) model for training to judge the working state and assembly quality problem type of combine. The results show that the accuracy of using the unfused entropy features is 82.5%, the accuracy of the fused features is 87.9%. After WOA optimization, the accuracy of the final classification reaches 92.1%, which is better than 89.6% by GA and 90.5% by PSO. It shows that the method proposed in this paper can accurately identify combine problems with different conditions and can be applied to combine assembly quality detection.

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Diagnosis of mechanical defects using a hybrid method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and optimized wavelet multi-resolution analysis (OWMRA): experimental study

Cited by 13 publications

References 32 publications

Research on Wheel-Rail Local Impact Identification Based on Axle Box Acceleration

Research on Wheel-Rail Local Impact Identification Based on Axle Box Acceleration

Study on Wear Mechanism of Helical Gear by Three-Body Abrasive Based on Impact Load

Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

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