Composite Fault Diagnosis Based on Deep Convolutional Generative Adversarial Network

Zhang, Yonghong; Zhang, Zhongyang; Fan, Shuiyong; Wang, Yifei; Zhao, Xiaoping; Kaiyang, Lv

doi:10.1109/aparm49247.2020.9209365

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Yao et al [11] proposed a CNN-based composite fault diagnosis method that converts bearing vibration signals into grayscale maps as training samples for the network, which can effectively identify bearing hybrid faults in urban rail trains. Zhang et al [12] considered a deep convolutional generative adversarial network model under insufficient diagnostic samples and effectively improved the composite fault diagnosis by generating additional composite fault data samples. Sun et al [13] combined an improved particle swarm-optimized variational modal decomposition with CNN to achieve mixed fault diagnosis of planetary gearboxes.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Zhang¹,

Jiang²,

Xu³

et al. 2023

Preprint

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To realize the diagnosis of compound faults in gearboxes at different speeds, an "end-to-end" intelligent diagnosis method based on a deep neural network is proposed, named efficiency channel attention-capsule network (ECA-CN). First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, then embeds the efficient channel attention module to filter important fault features, then uses the capsule network to vectorize the feature space information, and finally calculates the correlation between different levels of capsules by the dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method was verified by the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63% and a standard deviation of 0.22%. In the comparison experiments with the traditional fault diagnosis method, the average accuracy of the ECA-CN method was improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that ECA-CN has a more competitive diagnostic performance.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Zhang¹,

Jiang²,

Xu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Yao et al [15] proposed a CNN-based composite fault diagnosis method that converts bearing vibration signals into grayscale maps as training samples for the network, which can effectively identify bearing hybrid faults in urban rail trains. Zhang et al [16] considered a deep convolutional generative adversarial network model in the case of insufficient diagnostic samples and effectively improved the composite fault diagnosis by generating additional composite fault data samples. Sun et al [17] combined an improved particle swarm optimized variational modal decomposition with a CNN to achieve the mixed fault diagnosis of planetary gearboxes.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Zhang

Hong

et al. 2023

Energies

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Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds, an “end-to-end” intelligent diagnosis method based on an efficient channel attention capsule network (ECA-CN) is proposed. First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, embeds the efficient channel attention module to filter important fault features, uses the capsule network to vectorize the feature space information and, finally, calculates the correlation between different levels of capsules using a dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method is verified using the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63 ± 0.22%. In the comparison experiments using the traditional fault diagnosis method, the average accuracy of the ECA-CN method improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that the ECA-CN has a more competitive diagnostic performance than traditional shallow machine learning models and CNNs.

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