Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

Liao, Mengliang; Liu, Chang; Yang, Jianwei

doi:10.1109/access.2021.3067152

Cited by 17 publications

(7 citation statements)

References 34 publications

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“…Otherwise go to step 4). 7) Threshold processing is performed on the wavelet coefficients of each scale using AETF by (13), and the estimated wavelet coefficients are subjected to inverse wavelet transform. 8) Judge whether the SNR and RMSE meet the requirements.…”

Section: Fault Diagnosis Process Based On Adaptive Exponential Wavelet Threshold Denoising Methodsmentioning

confidence: 99%

“…A new noise-controlled second-order enhanced stochastic resonance (SR) method based on the Morlet wavelet transform was proposed to enhance the weak fault identification, and it could extract fault feature of the looseness fault for wind turbine shaft coupling [10]. In conclusion, the above research shows that wavelet analysis is a useful and effective time-frequency method for wind turbine incipient fault detection, and it is mainly applied to feature separation and noise elimination [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Kong

Tongle

et al. 2021

IEEE Access

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Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied to the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. And the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the preexisting algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.INDEX TERMS Adaptive wavelet threshold, dynamic weight particle swarm optimization, fault diagnosis, sigmoid-based acceleration coefficients, wind turbine bearing.

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Section: Fault Diagnosis Process Based On Adaptive Exponential Wavelet Threshold Denoising Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Kong

Tongle

et al. 2021

IEEE Access

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show abstract

“…The output of the FC-layer is mostly obtained with the softmax classifier that is suitable for multi-type classification. The function of softmax is described as follows [ 39 ]:

where

represents the parameters of the fully connected layer,

is the

th input of fully connected layer,

is a vector of length with range (0, 1), and

represents the

-category classification.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network

Gao²,

Zhang³

et al. 2022

Entropy

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Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial practice, the running state of bearings is monitored by collecting data from multiple sensors, for instance, the drive end, the fan end, and the base. Given the complexity of the operating conditions and the limited number of bearing-fault samples, obtaining complementary fault features using the traditional fault-diagnosis method, which uses statistical characteristic in time or frequency, is difficult and relies heavily on prior knowledge. In addition, intelligent bearing-fault diagnosis based on a convolutional neural network (CNN) has several deficiencies, such as single-scale fixed convolutional kernels, excessive dependence on experts’ experience, and a limited capacity for learning a small training dataset. Considering these drawbacks, a novel intelligent bearing-fault-diagnosis method based on signal-to-RGB image mapping (STRIM) and multichannel multiscale CNN (MCMS-CNN) is proposed. First, the signals from three different sensors are converted into RGB images by the STRIM method to achieve feature fusion. To extract RGB image features effectively, the proposed MCMS-CNN is established, which can automatically learn complementary and abundant features at different scales. By increasing the width and decreasing the depth of the network, the overfitting caused by the complex network for a small dataset is eliminated, and the fault classification capability is guaranteed simultaneously. The performance of the method is verified through the Case Western Reserve University’s (CWRU) bearing dataset. Compared with different DL approaches, the proposed approach can effectively realize fault diagnosis and substantially outperform other methods.

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“…Max pooling layer often follows the convolutional layer, which decreases the redundancy and avoids over-fitting from extracted features [9]. In this architecture, max pooling is used to detect maximum values from previous output:…”

Section: Pooling Layermentioning

confidence: 99%

“…Traditional Convolutional Neural Networks (CNN) method had a widely application in fault classification, however, these studies used noise data and decreased selflearning ability for feature information [7][8][9][10]. Therefore, Deep Convolutional Neural Network (DCNN) method was developed to stronger the extraction feature ability from noise data and improve deep learning quality [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method Based on MADCNN for Rolling Bearings

Gao

Piltan

Ahmad

et al. 2022

Machine Learning and Artificial Intelligence

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Rolling bearings are treated as important machinery power components, faults of rolling bearings affect machinery operation, so an intelligent fault diagnosis method is very useful of safety operation in rolling bearings. This paper proposes a novel fault diagnosis method based on improved Adaptive Deep Convolution Neural Networks algorithm to realize fault recognition for rolling bearings. First, the Continuous Wavelet Transform (CWT) method is applied to the time-frequency decomposition of vibration signals and extract feature information images for training and testing. Second, to further improve self-learning ability of the Adaptive Deep Convolution Neural Network (ADCNN) in feature images, the Multiple Channels ADCNN method is proposed to classify different fault image types for the rolling bearing. Finally, fault images corresponding to different health states of the rolling bearing are applied to the proposed method, the experiment proves that the proposed method has a better performance for fault recognition in rolling bearings.

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Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

Abstract: Date of publication, date of current version.

Cited by 17 publications

References 34 publications

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network

A Novel Fault Diagnosis Method Based on MADCNN for Rolling Bearings

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