Fault Diagnosis and Prediction of Rolling Bearings Based on Deep Learning

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

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Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽

10.3390/sym14010013 ◽

2021 ◽

Vol 14 (1) ◽

pp. 13

Author(s):

Jianpeng Ma ◽

Chengwei Li ◽

Guangzhu Zhang

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Information Fusion ◽

Rolling Bearing ◽

Fusion Method ◽

Rolling Bearings ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Evolution Analysis ◽

Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

Measurement ◽

10.1016/j.measurement.2021.110500 ◽

2021 ◽

pp. 110500

Author(s):

Jun Li ◽

Yongbao Liu ◽

Qijie Li

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Imbalanced Data ◽

Learning Method ◽

Intelligent Fault Diagnosis ◽

Rolling Bearings

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A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Sensors ◽

10.3390/s20174965 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4965 ◽

Cited By ~ 4

Author(s):

Shoucong Xiong ◽

Hongdi Zhou ◽

Shuai He ◽

Leilei Zhang ◽

Qi Xia ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Frequency Analysis ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Rolling Bearings ◽

Time Frequency Analysis ◽

Time Frequency ◽

End To End

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

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Fault diagnosis based on deep learning by extracting inherent common feature of multi-source heterogeneous data

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820933380 ◽

2020 ◽

pp. 095965182093338

Author(s):

Funa Zhou ◽

Shuai Yang ◽

Yifan He ◽

Danmin Chen ◽

Chenglin Wen

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Heterogeneous Data ◽

Feature Representation ◽

Rolling Bearings ◽

Alternating Optimization ◽

Feature Extraction Method ◽

Diagnosis Method ◽

Diagnosis Accuracy ◽

Optimization Mechanism

Fault diagnosis can provide a basic guarantee for safe operation of industrial equipment. Deep learning has attracted much attention from experts in the field of fault diagnosis because of its powerful feature representation ability. But traditional deep learning methods cannot well extract common feature from multi-source heterogeneous data which is the inherent character of the monitored object. Using only one kind of heterogeneous data for deep learning fault diagnosis will inevitably result in poor diagnosis accuracy. Aiming at this problem, this article proposes a deep common feature extraction method by designing a fusion network with alternating optimization mechanism. The rough features extracted independently from two kinds of heterogeneous data are used to train the designed fusion network in an alternative optimization way. A new loss function required by alternative optimization is established; thus, all the networks can be tuned globally. The deep common features of multi-source heterogeneous data can be well extracted by alternating optimization training process of the fusion network, which improves the accuracy of deep learning fault diagnosis method. Experiments for rolling bearings fault diagnosis testify the effectiveness of the proposed algorithm.

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A hybrid deep-learning model for fault diagnosis of rolling bearings

Measurement ◽

10.1016/j.measurement.2020.108502 ◽

2021 ◽

Vol 169 ◽

pp. 108502 ◽

Cited By ~ 2

Author(s):

Yang Xu ◽

Zhixiong Li ◽

Shuqing Wang ◽

Weihua Li ◽

Thompson Sarkodie-Gyan ◽

...

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Learning Model ◽

Rolling Bearings ◽

Deep Learning Model

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Multisource Data Fusion Diagnosis Method of Rolling Bearings Based on Improved Multiscale CNN

Journal of Sensors ◽

10.1155/2021/2251530 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Yulin Jin ◽

Changzheng Chen ◽

Siyu Zhao

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Data Fusion ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Feature Maps ◽

Rolling Bearings ◽

Global Average

Intelligent diagnosis applies deep learning algorithms to mechanical fault diagnosis, which can classify the fault forms of machines or parts efficiently. At present, the intelligent diagnosis of rolling bearings mostly adopts a single-sensor signal, and multisensor information can provide more comprehensive fault features for the deep learning model to improve the generalization ability. In order to apply multisensor information more effectively, this paper proposes a multiscale convolutional neural network model based on global average pooling. The diagnostic model introduces a multiscale convolution kernel in the feature extraction process, which improves the robustness of the model. Meanwhile, its parallel structure also makes up for the shortcomings of the multichannel input fusion method. In the multiscale fusion process, the global average pooling method is used to replace the way to reshape the feature maps into a one-dimensional feature vector in the traditional convolutional neural network, which effectively retains the spatial structure of the feature maps. The model proposed in this paper has been verified by the bearing fault data collected by the experimental platform. The experimental results show that the algorithm proposed in this paper can fuse multisensor data effectively. Compared with other data fusion algorithms, the multiscale convolutional neural network model based on global average pooling has shorter training epochs and better fault diagnosis results.

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