Power Plant Production Equipment Sound Recognition Method Combined with Attention Mechanism

Feng, Zhiyan; Zhao, Zengtao; Chen, Honghao; Dou, Bowen; Hu, Liehao

doi:10.1109/icpics55264.2022.9873701

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Human beings can easily distinguish voiceprint signals in the low-frequency range but are often insensitive to sound in the high-frequency range. To find the voiceprint features suitable for human resolution, the common frequency scale is often converted into the Mel frequency scale to form a linear relationship between the human ear’s perception of frequency and make it easier to detect the frequency changes of high-frequency voiceprint signals [ 17 , 18 ]. The typical fault voiceprint frequency of power transformer mainly occurs in medium and low frequencies below 2000 Hz.…”

Section: Voiceprint Signal Preprocessing and Classifiermentioning

confidence: 99%

Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement

Wan

Dong

Zhang

et al. 2023

Sensors

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A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed. First, the parallel Mel filter is used to reduce the dimension of the fault voiceprint signal to obtain the Mel time spectrum. Then, the Mixup data enhancement algorithm is used to reorganize the generated small number of samples, effectively expanding the number of samples. Finally, CNN is used to classify and identify the transformer fault types. The diagnosis accuracy of this method for a typical unbalanced fault of a power transformer can reach 99%, which is superior to other similar algorithms. The results show that this method can effectively improve the generalization ability of the model and has good classification performance.

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Section: Voiceprint Signal Preprocessing and Classifiermentioning

confidence: 99%