A rolling bearing fault diagnosis method based on Markov transition field and multi-scale Runge-Kutta residual network

Ding, Simin; Rui, Zhiyuan; Lei, Chunli; Zhuo, Junting; Shi, Jiashuo; Lv, Xin

doi:10.1088/1361-6501/acf8e7

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…The original signal is converted into MTF 2D map and then input into 2DCNN for training; (6) MTF-MDCNN [25]. The original signal is converted into MTF 2D maps and then input into a multi-dimensional CNN for training; (7) MTF-ResNet [24]. The original signal is converted to MTF 2D map and then input into ResNet for training; (8) MCCNN-AM, our method.…”

Section: Comparative Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Alsalaet et al [23] proposed a bearing fault diagnosis method based on 2D coding graphs. Ding et al [24] used Markov transition field (MTF) to convert the original vibration signal into a 2D image, which achieved high diagnostic accuracy under both given and variable operating conditions. Lei et al [25] proposed a multidimensional deep learning method based on MTF, which has high diagnostic accuracy under variable operating conditions.…”

Section: Introductionmentioning

confidence: 99%

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A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Hu,

Liu,

Zhao

et al. 2024

Meas. Sci. Technol.

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Recently, deep learning has received widespread attention in the field of bearing fault diagnosis due to its powerful feature learning capability. However, when the actual working conditions are complex and variable, the fault information in a single domain is limited, making it difficult to achieve high accuracy. To overcome these challenges, this paper proposes a bearing fault diagnosis method based on the Markov transition field, continuous wavelet transform (CWT), and dual-channel convolutional neural network (CNN). The method combines the descriptive ability of the Markov model for state transfer, the time-frequency analysis ability of CWT for signal, and the excellent performance of CNN with attention mechanism in feature extraction and classification. Specifically, we first propose a multi-channel Markov transition field method, which is combined with CWT to obtain two different representations of two-dimensional (2D) images. To comprehensively mine fault information, we further propose a dual-channel CNN with an attention mechanism. The design of this network structure aims to extract multi-level features from two types of 2D images. At the same time, we designed and embedded an attention mechanism to enable the network to focus more on extracting effective features, thereby improving the performance and accuracy of the network. To verify the effectiveness of the proposed method, three datasets were used for empirical research. The results show that this method exhibits superior performance in bearing fault diagnosis and has higher accuracy compared to traditional methods.

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Section: Comparative Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Hu,

Liu,

Zhao

et al. 2024

Meas. Sci. Technol.

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show abstract

“…The current mainstream image encoding methods include Markov Transition Field (MTF) [29], Gram Angle Field(GAF) [30], Recurrence Plot (RP) [31], Relative Position Matrix (RPM) [32], and Hilbert-Huang Transform (HHT) [33]. To realize bearing fault diagnostics [34], combines (MTF) and multi-scale Runge-Kutta residual network (MRKRA-Net). [35], suggests using CNN and the optimal Hilbert curve (OHC) approach for bearing fault identification.…”

Section: Introductionmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

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Bearing fault diagnosis is of great significance to the normal operation of machinery, and its performance and life span directly affect the operational efficiency and safety of the whole equipment. For existing image coding methods which detecting the bearing fault with a large number of training samples and complex neural networks to achieve the desired detection performance, the Multiscale Permutation Entropy Gray Image Coding (MPEGIC) method is proposed. In order to fully extract the feature information of the time series signal, this paper uses the Multiscale Permutation Entropy (MPE) method to construct a new image coding method by calculating the alignment information of the time series to reflect the complexity and randomness of the time series at different scales, and dividing the obtained feature matrix and mapping it to the gray-scale image domain. And it is experimentally verified by Case Western Reserve University (CWRU) bearing dataset and self-made rotor experimental platform bearing dataset. The results show that the method in this paper effectively reduces the number of training samples and the number of model parameters, and maintains a better detection performance even in a strong noise background.

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“…However, in practical conditions, image signals are easily disturbed due to occlusion, and real-time performance is difficult to guarantee; optical sensing devices are expensive and require high illumination conditions. Therefore, recognition has often been accomplished in recent years through time-series signals, which have economic, real-time, and reliable advantages compared to other methods [12,13]. Wei et al [14] proposed an improved empirical variational mode decomposition (EVMD) method.…”

Section: Introductionmentioning

confidence: 99%

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

Wang,

Zhang,

Cao

et al. 2024

Meas. Sci. Technol.

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To address the low accuracy of current one-dimensional signal recognition for coal-rock cutting vibration and the low efficiency of traditional static neural networks, this paper proposes an interpretable recognition method that combines Markov Transition Field (MTF) and channel-selective neural networks. Firstly, by decomposing and reconstructing the energy of wavelet packets and denoising the signal, it is transformed into MTF images with temporal correlation to improve signal processing efficiency. Then, a channel selective module is proposed to replace traditional convolutional layers, enhancing the model's ability to extract data features. A spatial shift mechanism is improved to alleviate the problem of weight degradation and improve the model's generalization capability. The test results on a self-constructed experimental dataset show that this method achieves a recognition accuracy of up to 97.375% without increasing additional parameters, significantly outperforming traditional methods and maintaining good robustness in noisy environments. This study improves the efficiency and accuracy of coal-rock cutting state recognition and provides new ideas for signal processing in environments with limited underground computing resources.

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A rolling bearing fault diagnosis method based on Markov transition field and multi-scale Runge-Kutta residual network

Cited by 7 publications

References 38 publications

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

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