Multiple measurement vector compressive sampling and fisher score feature selection for fault classification of roller bearings

Ahmed, Hosameldin

doi:10.1109/icdsp.2017.8096076

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…SVM can execute linear or nonlinear classification through the utilization of diverse kernel functions, such as the radial basis function, polynomial function and sigmoid function. SVM classifiers can be effectively combined using either the one-vs-all or one-vs-one approach (Ahmed and Nandi, 2017). SVM can be represented as follows: where x is an M-dimensional vector independent variable, ω is a vector that has the same size with x and b is a scalar, the hyperplane is defined by w and b .…”

Section: Fault Classificationmentioning

confidence: 99%

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Taibi,

Touati,

Aomar

et al. 2024

COMPEL

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Purpose Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine. Design/methodology/approach To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification. Findings The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals. Originality/value This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

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Section: Fault Classificationmentioning

confidence: 99%

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Taibi,

Touati,

Aomar

et al. 2024

COMPEL

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show abstract

“…These studies showed that the frequency domain analysis techniques can reveal information from vibration signals that are not easy to be observed in the time domain. For example, Fourier analysis including Fourier series, Discrete Fourier Transform (DFT), and Fast Fourier Transform (FFT) techniques are used to transform time-domain vibration signals to the frequency domain [12][13][14][15][16][17][18]. Moreover, various techniques are used to extract different spectrum features to represent a bearing's health condition.…”

Section: Introductionmentioning

confidence: 99%

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Ahmed

Nandi

2021

Chin. J. Mech. Eng.

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Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

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Compressive Sampling and Subspace Learning ( CS ‐ SL )

2019

Condition Monitoring With Vibration Signals

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Multiple measurement vector compressive sampling and fisher score feature selection for fault classification of roller bearings

Cited by 7 publications

References 26 publications

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Compressive Sampling and Subspace Learning ( CS ‐ SL )

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