Sequential Mechanical Fault Diagnosis in High Voltage Circuit Breaker using Attention Mechanism

Zhang, Baoyu; Wang, Xinru; Wang, Jin

doi:10.1088/1742-6596/2003/1/012016

Cited by 2 publications

(2 citation statements)

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“…Zhou et al enhanced the traditional CNN model with an identity mapping module to improve feature extraction and increase the recognition rate of HVCB fault diagnosis [19]. To complete the state diagnosis of HVCBs, Zhang et al used the attention mechanism to locally weigh the correlation of samples and the long short-term memory network to optimize the traditional CNN model [20]. Under certain experimental conditions, the above CNN-based fault diagnosis models can achieve better diagnosis results.…”

Section: Introductionmentioning

confidence: 99%

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A novel circuit breaker fault diagnosis method based on dense residual and attention mechanism

Ye,

Yan,

Wang

et al. 2023

IET Generation Trans & Dist

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In recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability.

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Section: Introductionmentioning

confidence: 99%

“…To complete the state diagnosis of HVCBs, Zhang et al. used the attention mechanism to locally weigh the correlation of samples and the long short‐term memory network to optimize the traditional CNN model [20]. Under certain experimental conditions, the above CNN‐based fault diagnosis models can achieve better diagnosis results.…”

Section: Introductionmentioning

confidence: 99%