Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Yang, Jingli; Gao, Tianyu; Jiang, Shouda; Li, Shijie; Tang, Qing

doi:10.1155/2020/8880960

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…The BN layer can reduce the internal covariates and speed up the training and convergence of the model while suppressing the overfitting problem to some extent. By applying the AdaBN algorithm to the target domain data and the BN to the source domain data, it is possible to map the source domain data and the target domain data to a new distribution space in which the source and target domain data distributions are approximately the same, and thus the domain adaptation of the algorithmic model can be achieved [26].…”

Section: Adabn Algorithmmentioning

confidence: 99%

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Yang,

Li,

Wen

et al. 2023

Meas. Sci. Technol.

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In this paper, we propose a one-dimensional convolutional neural network(1D-CNN) model based on the adaptive batch normalization (AdaBN) algorithm to improve the CNN model, which is difficult to extract features from multi-rotor unmanned aerial vehicle (UAV) rotor structural faults under variable conditions and has poor fault diagnosis performance. The method accomplishes fault diagnosis and classification by feature extraction from lower dimensional multi-rotor UAV data. The AdaBN algorithm adjusts the parameters of the BN layer in the model during the testing phase to improve the domain adaptive capability of the model in scenarios with variable operating conditions. Also, to improve the robustness of the model under noisy conditions, the first layer of convolutional kernel dropout operation is introduced to improve the noise immunity of the model. To reduce the complexity of manual tuning and to find the optimal combination of hyperparameters for the network model more effectively, the grey wolf optimizer algorithm is used to optimize the hyperparameters and further improve the model performance. Finally, the effectiveness of the proposed method is verified through comparison tests, and it shows good diagnostic effects in noise and variable conditions compared with several commonly used fault diagnosis methods.

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Section: Adabn Algorithmmentioning

confidence: 99%

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Yang,

Li,

Wen

et al. 2023

Meas. Sci. Technol.

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show abstract

“…Deep residual shrinkage networks are widely used in fault diagnosis [ 32 , 33 , 34 ], in which deep residual shrinkage networks recognize the target signal from noise. Yang et al [ 35 ] introduced a fault diagnosis method of rotating machinery based on one-dimensional deep residual shrinkage networks, which significantly improves fault diagnosis accuracy.…”

Section: Related Workmentioning

confidence: 99%

DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion

Wang

et al. 2022

Sensors

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Infrared images are robust against illumination variation and disguises, containing the sharp edge contours of objects. Visible images are enriched with texture details. Infrared and visible image fusion seeks to obtain high-quality images, keeping the advantages of source images. This paper proposes an object-aware image fusion method based on a deep residual shrinkage network, termed as DRSNFuse. DRSNFuse exploits residual shrinkage blocks for image fusion and introduces a deeper network in infrared and visible image fusion tasks than existing methods based on fully convolutional networks. The deeper network can effectively extract semantic information, while the residual shrinkage blocks maintain the texture information throughout the whole network. The residual shrinkage blocks adapt a channel-wise attention mechanism to the fusion task, enabling feature map channels to focus on objects and backgrounds separately. A novel image fusion loss function is proposed to obtain better fusion performance and suppress artifacts. DRSNFuse trained with the proposed loss function can generate fused images with fewer artifacts and more original textures, which also satisfy the human visual system. Experiments show that our method has better fusion results than mainstream methods through quantitative comparison and obtains fused images with brighter targets, sharper edge contours, richer details, and fewer artifacts.

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“…In this paper, a series of experiments are carried out to determine the values of relevant parameters of the model. Each group of experiments is carried out for 5 times, and the average accuracy of 5 experiments is taken as the evaluation standard of the model [35]. The parameters considered in the experiment are: the size of the first wide convolution kernels, the number of the wide convolution kernels, the number of residual modules, and the block size discarded by the DropBlock layer.…”

Section: Model Determination Experiments Of Quadrotor Fault Diagnosis Based On 1d-widrsnmentioning

confidence: 99%

An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Yang

Geng

Wen

et al. 2021

Drones

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In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.

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Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Cited by 13 publications

References 37 publications

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion

An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

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