A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Hoang, Duy Tang; Tran, Xuan-Toa; Van, Mien

doi:10.3390/s21010244

Cited by 38 publications

(20 citation statements)

References 28 publications

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“…where f 1 is the coefficient related to bearing type and bearing load, and p 1 is the load (unit: N) for calculating the bearing friction torque. After determining the bearing friction torque, the calorific value generated by frictional heating is calculated according to (17):…”

Section: Dynamic Simulation Modelmentioning

confidence: 99%

“…For example, Gao et al used the rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition [16]. Hoang et al observed that the convolutional neural network processed multiple signal sources simultaneously and was constrained by sensor functions [17]. Jiao et al proposed a new bearing fault diagnosis method based on the least squares support vector machine for feature-level fusion and the Dempster-Shafer (DS) evidence theory for decision-level fusion [18].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

2021

Symmetry

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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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Section: Dynamic Simulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

2021

Symmetry

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“…For 2D-CNN models, vibration data cannot be used in their raw form. Rather, they are initially converted into time-frequency image representations such as spectrograms [38][39][40], scalograms [40,41] or other types of vibration images [42]. Then these images which represent the vibration signal in image form are used as input in 2D-CNN model.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps

Tayyab

Chatterton

Pennacchi

2022

Sensors

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Vibration analysis is an established method for fault detection and diagnosis of rolling element bearings. However, it is an expert oriented exercise. To relieve the experts, use of Artificial Intelligence (AI) techniques such as deep neural networks, especially convolutional neural networks (CNN) have gained much attention of the researchers because of their image classification and recognition capability. Most of the researchers convert the vibration signal into representative time frequency vibration images such as spectrogram, and scalogram. These images are used as input to train the CNN model for fault diagnosis. Commonly, fault diagnosis is performed under same operating conditions, where models are trained and deployed for prediction under same operating conditions. However, outside laboratory environment, in real world applications, different operating conditions, such as variable speed, may be encountered. With the change in speed, characteristic frequencies of vibration signal will also change, which will result in changing the vibration image. Consequently, performance of CNN model may drop significantly for prediction under different operating conditions. To get the training data from all potential operating conditions may not be feasible for most of the real-world applications. Therefore, there is need to find some signal properties which are invariant to change in operating conditions and only change due to change in health state. So that models trained under one set of operating conditions may predict correctly under different operating conditions. This paper proposes a defect diagnosis method for rolling element bearings, under variable operating conditions (speed and load) based on CNN and order maps. These maps exhibit consistent properties under varying speed; therefore, they can be used to train the CNN model for fault diagnosis under variable speed. Effect of load change on these order maps is experimentally studied and it is found that proposed method can do fault diagnosis of rolling element bearings under variable speed and load with high accuracy.

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“…Deep neural networks have extensive applications in artificial intelligence mainly including computer vision [1][2][3][4][5][6][7][8], speech recognition [9][10][11][12][13], medical detection [14][15][16][17][18][19], and mechanical fault diagnosis [20][21][22][23][24][25][26][27][28]. Compared with human ability, the DNN model is more capable of solving this complicated problem.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Learning Algorithm for Machine Fault Diagnosis

Dong

Liu

Kang

et al. 2022

Shock and Vibration

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Industrial big data bring a large number of high-dimensional sample datasets. Although a deep learning network can well mine the internal nonlinear structure of the dataset, the construction of the deep learning model requires a lot of computing time and hardware facilities. At the same time, there are some nonlinear problems such as noise and fluctuation in industrial data, which make the deep architecture extremely complex and the recognition accuracy of the diagnosis model difficult to guarantee. To solve this problem, a new method, named stochastic learning algorithm (SL), is proposed in this paper for dimension reduction. The proposed method consists of three steps: firstly, to increase the computational efficiency of the model, the dimension of the high-dimensional data is reduced by establishing a random matrix; secondly, for enhancing the clustering influence of the sample, the input data are enhanced by feature processing; thirdly, to make the clustering effects more pronounced, the noise and interference of the data need to be processed, and the singularity value denoising method is used to denoise training data and test data. To further prove the superiority of the SL method, we conducted two sets of experiments on the wind turbine gearbox and the benchmark dataset. It can be seen from the experimental results that the SL method not only improves the classification accuracy but also reduces the computational burden.

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A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Cited by 38 publications

References 28 publications

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

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

Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps

A Stochastic Learning Algorithm for Machine Fault Diagnosis

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