Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings

Xie, Songyun; Ren, Guoying; Zhu, Junjiang

doi:10.1177/0036850420951394

Cited by 15 publications

(17 citation statements)

References 26 publications

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“…For a further comparison of the solving accuracy, some other theoretical methods [27][28][29][30][31], such as the D-CNN model [29] and the CNN-SVM model [30] are considered. Table 2 indicates the results of classification accuracy and calculation time.…”

Section: A Comparison Of the Eoretical Methodsmentioning

confidence: 99%

A Novel Sparse Enhancement Neural Network for Rolling Bearing Fault Diagnosis

Zhang

Yang

et al. 2022

Shock and Vibration

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To ensure the operational reliability of machinery, rolling bearings exposed to complex and poor conditions should be monitored in real-time. Traditional bearing fault diagnosis methods are always dependent on signal analysis and feature extraction, which are complex and time-consuming. Deep learning method exhibits a good ability in extracting the fault feature, while it is limited to noise pollution and insufficient sample data during the training procedure. In this study, a new sparse enhancement neural network based on generalized minimax-concave penalty and convolutional neural network is proposed to capture fault features automatically. To this end, the generalized minimax-concave penalty is first employed to expand the dataset by pollution data denoise and sparse enhancement of the insufficient samples. Second, the amplified dataset is employed to train the fault classification. By employing the datasets of drive end and fan end derived from the Case Western Reserve University (CWRU), a good prediction accuracy can be found in fault diagnosis for rolling bearings.

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Section: A Comparison Of the Eoretical Methodsmentioning

confidence: 99%

A Novel Sparse Enhancement Neural Network for Rolling Bearing Fault Diagnosis

Zhang

Yang

et al. 2022

Shock and Vibration

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“…e traditional methods include the self-organizing selection center method and random selection method, both of which have some limitations. e former is more complex in the calculation, while the latter is generally applicable to the representative sample distribution [16].…”

Section: Complexitymentioning

confidence: 99%

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Jian-ping

Gao

2021

Complexity

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Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is 5.921 × 10 − 4 , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.

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“…Azamfar, M., et al put forward a method based on motor current signature analysis and 2-D convolutional neural network used for gearbox fault diagnosis [32]. Xie, S., et al presented a new convolutional neural network with a one-dimensional structure (ODCNN) for the automatical fault diagnosis of rolling bearings [33]. However, these methods have too many parameters, and the network convergence speed is slow and cannot be used to practical projects.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings

Cited by 15 publications

References 26 publications

A Novel Sparse Enhancement Neural Network for Rolling Bearing Fault Diagnosis

A Novel Sparse Enhancement Neural Network for Rolling Bearing Fault Diagnosis

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

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

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