Research on Electrical Parameter Fault Diagnosis Method of Oil Well Based on TSC-DCGAN Deep Learning

Bai, Tingting; Li, Xiangyu; Ding, Shihong

doi:10.1109/icbaie56435.2022.9985784

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…In 2022, H. Hu [13] proposed a model based on the ResNet-34 residual network to identify the indicator diagrams, which added a residual block structure to the traditional convolution neural network to establish a direct connection between the upper layer input and the lower layer output and achieved the recognition and classification of six power diagrams through parameter adjustment. In the same year, T. Bai [14] proposed a fault diagnosis method based on a time series transformation generative adversarial network (TSC-DCGAN).…”

Section: Introductionmentioning

confidence: 99%

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

Zhang,

Li,

Shan

et al. 2023

Processes

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Due to the complex underground environment, pumping machines are prone to producing numerous failures. The indicator diagrams of faults are similar to a certain degree, which produces indistinguishable samples. As the samples increase, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. To accurately and quickly judge the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the Rectifying linear unit activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared to the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity and can improve the generalization performance of the deep learning model and the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well-embedded into other neural networks.

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Section: Introductionmentioning

confidence: 99%

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

Zhang,

Li,

Shan

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networks

Zou,

Ma,

et al. 2024

Applied Soft Computing

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An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

Zhang¹,

Li²,

Shan³

et al. 2023

Preprint

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Due to the complex underground environment, pumping machines are prone to produce numerous failures. The indicator diagrams of faults are similar in a certain degree, which produces indistinguishable samples. As the samples increases, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. For accurately and quickly judging the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the ReLU activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared with the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity, can improve the generalization performance of deep learning model, the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well embedded in other neural networks.

show abstract

Research on Electrical Parameter Fault Diagnosis Method of Oil Well Based on TSC-DCGAN Deep Learning

Cited by 3 publications

References 14 publications

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networks

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

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