Bearing Faults Classification Based on Variational Mode Decomposition and Artificial Neural Network

Guedidi, Asma; Guettaf, Abderrazak; Cardoso, António J. Marques; Laala, W.; Arif, Arshia

doi:10.1109/demped.2019.8864830

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…A ball bearing of type 6205 is used, whose specification is mentioned in Table 1. In this study, data of bearing at the drive end, consisting of defects of diameter 177.8 In this study, VMD is used to generate 5 levels of IMFs, Guedidi et al 32 IMF's contains fault severity information and to find the optimal level, Maximum Energy to Shannon Entropy ratio criterion is used, so the IMF giving the maximum energy and least amount of Shannon entropy hase been selected for generation of Spectrogram.…”

Section: Experimentation and Proceduresmentioning

confidence: 99%

“…In this study, VMD is used to generate 5 levels of IMFs, Guedidi et al. 32 IMF’s contains fault severity information and to find the optimal level, Maximum Energy to Shannon Entropy ratio criterion is used, so the IMF giving the maximum energy and least amount of Shannon entropy hase been selected for generation of Spectrogram.…”

Section: Experimentation and Proceduresmentioning

confidence: 99%

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Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

Akhenia

Bhavsar

Panchal

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Condition monitoring and diagnosis of a bearing are very important for any rotating machine as it governs the safety while the machine is in operating condition. To construct a feature vector selection of suitable signal processing techniques is a challenge for vibration-based condition monitoring techniques. In the methodology proposed, Short Time Fourier Transform (STFT), Walsh Hadamard Transform (WHT) and Variable Mode Decomposition (VMD) are used to generate 2-D time-frequency spectrograms from the various fault conditions of bearing. When Deep learning techniques apply for fault diagnosis, a large amount of dataset is required for training of machine learning model. To overcome this issue single image Generative Adversarial Network (SinGAN) as a data augmentation technique, utilized for generating additional 2-D time-frequency spectrograms from various fault conditions of ball bearing. To detect fault severity, four deep learning algorithms, ResNet 34, ResNet50, VGG16, and MobileNetV2 are used as a classifier. Experiments are conducted on a rolling bearing dataset provided by the bearing data center of Case Western Reserve University (CWRU) for validating the utility of methodology proposed. Results show that the proposed methodology enables to detect fault severity level with high classification accuracy.

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Section: Experimentation and Proceduresmentioning

confidence: 99%

Section: Experimentation and Proceduresmentioning

confidence: 99%

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

Akhenia

Bhavsar

Panchal

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…However, there is another group of papers which is focused on machine learning (ML) and deep learning (DL) techniques [102]; to implement effective ML and DL algorithms for bearing fault detection, good data collection is needed and, therefore, these papers sometimes refer to datasets available on-line [103], but also to wide campaigns of laboratory measurements [104,105]. In [106], a very recent survey of these papers is reported, together with a comparative study of the classification accuracy of various algorithms that use the open-source Case Western Reserve University (CWRU) bearing dataset.…”

Section: Rolling Bearingsmentioning

confidence: 99%

Novel Diagnostic Techniques for Rotating Electrical Machines—A Review

Frosini

2020

Energies

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This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature.

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“…Compared with other time-frequency analysis directions, the VMD method has a complete theoretical foundation and good noise robustness. 13,14 At present, there have been a large number of studies applying the VMD method to early mechanical fault diagnosis and showing good results, [15][16][17] which provides a theoretical basis for us to use the VMD method to predict the degree of runner eccentricity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of runner eccentricity and Alford force of a Kaplan turbine based on variational mode decomposition

Hu,

Liu,

Wang

et al. 2024

Energy Science & Engineering

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The rotor blades of an axial‐flow turbine are cantilever structures, and there is inevitably a gap between them and the casing. Due to factors such as rotor wear and unit vibration, the eccentricity of the impeller will change during the operation of the turbine, resulting in the impeller being affected by additional radial forces, which can even lead to rubbing or biting between the impeller and the casing. To monitor the eccentricity of the impeller and the additional radial forces in real time during the operation of the turbine, this study conducted numerical simulations of the internal flow of the turbine under different eccentricities of the impeller, and analyzed the characteristics of pressure pulsation and impeller radial force in the turbine using the variational mode decomposition method. The results showed that there was a good linear relationship between the eccentricity of the impeller and the amplitude of the frequency corresponding to the rotor in pressure pulsation at the monitoring point and the Alford force acting on the impeller. Based on this finding, we established mathematical formulas for the relationship between the pressure pulsation at the monitoring point and the eccentricity of the impeller, as well as the eccentricity of the impeller and the Alford force acting on it. According to these formulas, we only need to monitor the pressure pulsation during the operation of the turbine to realize the real‐time monitoring of the eccentricity of the impeller and the Alford force, which is of great significance for ensuring the safe and stable operation of the turbine.

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Bearing Faults Classification Based on Variational Mode Decomposition and Artificial Neural Network

Cited by 15 publications

References 18 publications

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

Novel Diagnostic Techniques for Rotating Electrical Machines—A Review

Prediction of runner eccentricity and Alford force of a Kaplan turbine based on variational mode decomposition

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