Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

Zhao, Minghang; Tang, Baoping; Tan, Qian

doi:10.1088/0957-0233/26/8/085008

Cited by 16 publications

(15 citation statements)

References 44 publications

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“…The GLCM has been used to extract the PD features from the PRPD grayscale images effectively [15], [16], which has good PD feature distinguishability. However, the angle-offset parameter  used to calculate the GLCM has several values and is usually determined artificially, which results in a bad self-adaptability [17].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Transformers Based on the Gray-Level Co-Occurrence Matrix of Optimal Parameters

Sun

et al. 2021

IEEE Access

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The partial discharge (PD) is the most common fault of transformers, which is the main factor affecting the stable operation of transformers. Therefore, the PD should be monitored and identified timely to improve the reliability of the transformers. In this paper, a transformer PD pattern recognition algorithm based on the gray-level co-occurrence matrix of optimal parameters and support vector machine (GLCMOP-SVM) is proposed. Firstly, the GLCM of optimal parameters (GLCMOP) is proposed to be determined by calculating the proportion of the off-diagonal elements (PODE) in GLCM. The GLCMOP has the advantage of avoiding the subjectivity of parameter selection and simplifying the calculation process. Then, the phase-resolved partial discharge (PRPD) maps are used as the PD samples and are converted into the GLCMOP to extract the PD features. Moreover, the feature space of the GLCMOP is dimensionally reduced by screening out the features with high distinguishability, which can improve the generalization ability and recognition speed of the classifier. Finally, the SVM classifier is trained to sort the PD samples and recognize the PD types, which include the tip discharge, surface discharge, and air discharge PD types. Lab tests are performed to verify the accuracy and validity of the proposed methodology. Compared with the traditional algorithms based on GLCM, XGBoost (eXtreme Gradient Boosting) and artificial neural network (ANN), the performance of GLCMOP-SVM is better. The GLCMOP-SVM has less memory consumption and faster recognition speed, so it is very suitable for the online and real-time monitoring of PD occurred in the transformers.

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Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Transformers Based on the Gray-Level Co-Occurrence Matrix of Optimal Parameters

Sun

et al. 2021

IEEE Access

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“…Due to its severe working environment, the rolling bearing is prone to failure, which will result in economic losses and even security accidents [1]. Therefore, rolling bearing fault diagnosis has attracted much attention in recent years [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

Jiang

Shao

et al. 2018

Meas. Sci. Technol.

155

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Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

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“…Compared to the methods mentioned above, the time-frequency analysis methods are more effective when dealing with nonlinear and non-stationary signals, because they can accurately describe the local time-frequency characteristics of non-stationary signals by revealing the constituent frequency components and their time-variation characteristics. In fact, these methods are widely used in the field of mechanical fault diagnosis [20][21][22]. To date, the traditional time-frequency analysis methods, which are also commonly used methods, can be roughly divided into two classes [20]: linear time-frequency analysis methods, such as short-time Fourier transform (STFT), Gabor transform [23,24], wavelet transform (WT), and the S transform (ST); and bilinear/quadratic time-frequency analysis represented by the Wigner-Ville distribution (WVD) [22,[25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Chen

Meng

et al. 2018

Applied Sciences

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Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.

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Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

Cited by 16 publications

References 44 publications

Partial Discharge Pattern Recognition of Transformers Based on the Gray-Level Co-Occurrence Matrix of Optimal Parameters

Partial Discharge Pattern Recognition of Transformers Based on the Gray-Level Co-Occurrence Matrix of Optimal Parameters

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

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