scholarly journals Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hongwei Fan ◽  
Ceyi Xue ◽  
Xuhui Zhang ◽  
Xiangang Cao ◽  
Shuoqi Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Gaussian White Noise ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Model Parameters ◽  
Test Accuracy ◽  
Image Generation ◽  
Training Time ◽  
The Impact

Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition-pseudo-Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.

scholarly journals Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hong Pan ◽  
Wei Tang ◽  
Jin-Jun Xu ◽  
Maxime Binama
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Rolling Bearing ◽  
Convergence Speed ◽  
Learning Rate ◽  
Fault Classification ◽  
Model Parameters ◽  
Dynamic Learning ◽  
Stacked Autoencoder ◽  
Rate Adjustment

Fault diagnosis is of great significance for ensuring the safety and reliable operation of rolling bearing in industries. Stack autoencoder (SAE) networks have been widely applied in this field. However, the model parameters such as learning rate are always fixed, which have an adverse effect on the convergence speed and accuracy of fault classification. Thus, this paper proposes a dynamic learning rate adjustment approach for the stacked autoencoder network. First, the input data is normalized and enhanced. Second, the structure of the SAE network is selected. According to the positive and negative value of the training error gradient, a learning rate reducing strategy is designed in order to be consistent with the current operation of the network. Finally, the fault diagnosis models with different learning rate adjustment are conducted in order to validate the better performance of the proposed approach. In addition, the influence of quantities of labeled sample data on the process of backpropagation is analyzed. The results show that the proposed method can effectively increase the convergence speed and improve classification accuracy. Moreover, it can reduce the labeled sample size and make the network more stable under the same classification accuracy.

scholarly journals A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration

2021 ◽  
Vol 11 (3) ◽  
pp. 1251
Author(s):  
Kunlin Zhang ◽  
Wei Huang ◽  
Xiaoyu Hou ◽  
Jihui Xu ◽  
Ruidan Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Transfer Learning ◽  
High Speed ◽  
Visual Analysis ◽  
Monitoring Data ◽  
Fault Classification ◽  
Training Time ◽  
Diagnosis Model ◽  
Sensor Monitoring

Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.

scholarly journals An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Lévy Noise ◽  
Model Parameters ◽  
Rolling Bearings ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Levy Noise ◽  
The Impact

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

scholarly journals Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jun He ◽  
Xiang Li ◽  
Yong Chen ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Method ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

scholarly journals A transfer learning framework based on motor imagery rehabilitation for stroke

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fangzhou Xu ◽  
Yunjing Miao ◽  
Yanan Sun ◽  
Dongju Guo ◽  
Jiali Xu ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Stroke Rehabilitation ◽  
Transfer Functions ◽  
Model Parameters ◽  
Transfer Model ◽  
Learning Networks ◽  
Training Time ◽  
Fine Tune ◽  
Bci System

AbstractDeep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced ‘fine-tune’ to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and ‘fine-tune’ transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.

New transfer learning fault diagnosis method of rolling bearing based on ADC-CNN and LATL under variable conditions

Measurement ◽  
2021 ◽  
pp. 110587
Author(s):  
Chunran Huo ◽  
Quansheng Jiang ◽  
Yehu Shen ◽  
Chenhui Qian ◽  
Qingkui Zhang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Rolling Bearing ◽  
Diagnosis Method

scholarly journals Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianye Zhou ◽  
Xinyu Yang ◽  
Lin Zhang ◽  
Siyu Shao ◽  
Gangying Bian
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
High Efficiency ◽  
Image Resolution ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Multiple Sensors ◽  
Learning Framework ◽  
Training Samples ◽  
Model Training

To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy. First, the raw data collected by multiple sensors are combined into a graph and normalized to facilitate model training. Next, the transposed convolution is utilized to expand the image resolution, and then the images are treated as the input of the transfer learning model for training and fine-tuning. The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method. We have obtained advanced results on both datasets of the gearbox dataset. The dataset shows that the test accuracy is 99.99%, achieving a significant improvement from 98.07% to 99.99%.

scholarly journals Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Junfeng Guo ◽  
Xingyu Liu ◽  
Shuangxue Li ◽  
Zhiming Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Time Frequency ◽  
The Impact

As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

scholarly journals Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network

2021 ◽  
Vol 11 (22) ◽  
pp. 10889
Author(s):  
Xinyu Hao ◽  
Yuan Zheng ◽  
Li Lu ◽  
Hong Pan
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Testing Time ◽  
Residual Network ◽  
Rolling Bearings ◽  
Training Time ◽  
Whole Process ◽  
Model Training ◽  
Gap Technology ◽  
Fully Connected

Rolling bearings are the most fault-prone parts in rotating machinery. In order to find faults in time and reduce losses, this paper presents an intelligent diagnosis method for rolling bearings. At present, the deep residual network (RESNET) is the most widely used convolutional neural network (CNN) and has become one of the hotspots in fault diagnosis. However, the fully connected layer of the deep residual network has the disadvantage of too many training parameters, which makes the model training and testing time longer. So, we proposed a new network structure which the global average pooling (GAP) technology replaces the fully connected layer part of the traditional RESNET. It effectively solves the problem of too many parameters of the traditional RESNET model, and uses data enhancement, dropout, and other deep learning training techniques to prevent the model from overfitting. Experiments show that the accuracy of fault diagnosis of the improved algorithm reaches 99.83%, training time has been shortened. Also, the whole process of rolling bearing fault detection does not need any manually extract features, and this “end-to-end” algorithm has good versatility and operability.

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