Rolling‐Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network

Yang, Yanli; Fu, Peiying

doi:10.1155/2018/3047830

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…e number of neurons in the network will increase when the number of convolution kernels, the scale, and the number of network layers, or the step size of the convolution kernel, are reduced, which can increase the expressiveness of the network but at risk of overfitting. Other hyperparameters of the network need to be further adjusted in the experiment according to the amount of training data [28][29][30][31][32][33][34].…”

Section: Wfk-cnn Parametermentioning

confidence: 99%

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Lin

2020

Shock and Vibration

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It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

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Section: Wfk-cnn Parametermentioning

confidence: 99%

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Lin

2020

Shock and Vibration

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“…Wavelet packet analysis is a new method based on wavelet analysis [ 20 ] which can decompose the high-frequency and low-frequency parts of the signal more finely and conduct a more comprehensive signal analysis [ 21 ]. Each wavelet packet decomposition will obtain two sub-bands of low frequency and high frequency.…”

Section: Basic Principlesmentioning

confidence: 99%

Study on Wavelet Packet Energy Characteristics on Friction Signal of Lapping with the Fixed Abrasive Pad

et al. 2022

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The surface condition of the fixed abrasive pad (FAP) has a significant impact on its machining performance, workpiece material removal rate (MRR), and surface roughness. To clarify the wavelet packet energy characteristics of friction signal under different surface conditions of FAP and its mapping relationship with MRR and workpiece surface quality, FAP samples in different processing stages were obtained through a consolidated abrasive grinding quartz glass experiment. Then, the friction signals in different stages were received by the friction and wear experiment between the FAP and quartz glass workpiece, and the wavelet packet analysis was carried out. The experimental results show that with the increase of lapping time, the surface wear degree of the FAP increased gradually, and the MRR of the workpiece, the surface roughness of the FAP, and the surface roughness of the workpiece decreased slowly. In the wavelet packet energy of friction signal during machining, the energy proportion of frequency band 7 showed an upward trend with the increase of lapping time. The energy proportion of frequency band 8 showed a downward trend with the increase of lapping time. The change characteristics of the two are significantly correlated with the surface condition of the FAP.

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“…Using an automatic encoder, Jia et al [10] proposed an intelligent diagnostic method based on a deep neural network (DNN). Using a DNN, Yang et al proposed fault signal automatic classification methods in the frequency domain [11] and the wavelet domain [12]. Shao et al [13] used a three-layer DBN to diagnose rolling-element bearing faults.…”

Section: Introductionmentioning

confidence: 99%

A fault identification method based on an ensemble deep neural network and a correlation coefficient

Yang

2022

Soft Comput

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An intelligent fault identification algorithm based on ensemble deep neural networks and correlation coefficients is proposed for rotating machinery fault detection. In this algorithm, three deep neural networks (DNNs) are arranged to initially identify faults in the frequency domain, wavelet domain and envelope spectrum angle, respectively. Then, correlation coefficients are adopted to evaluate the recognition results of the DNNs. The reliabilities of the DNNs' recognition results are divided into 6 levels according to the outputs of the DNNs and the correlation coefficients. Finally, the evaluation results of the three DNNs are put through fusion processing to generate more reliable recognition results. This algorithm identifies fault signals from multiple angles and combines the correlation coefficients to make a comprehensive judgment, which is beneficial for fault identification. The proposed algorithm is tested with the bearing data provided by the Case Western Reserve University Bearing Data Center. The test results show that the algorithm has a high recognition accuracy for homologous data and a good generalization ability for nonhomologous data. 

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Rolling‐Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network

Cited by 7 publications

References 26 publications

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Study on Wavelet Packet Energy Characteristics on Friction Signal of Lapping with the Fixed Abrasive Pad

A fault identification method based on an ensemble deep neural network and a correlation coefficient

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