Fault diagnosis of GIS equipment based on voice print recognition and algorithm research

Xiaoliang Zhuang; Qiankun Li; Yanhui Shi; Xueming Huang; Chenjiarui Gong; Xiangdong Qi

doi:10.1117/12.3011223

1 December 2023 Fault diagnosis of GIS equipment based on voice print recognition and algorithm research

Xiaoliang Zhuang, Qiankun Li, Yanhui Shi, Xueming Huang, Chenjiarui Gong, Xiangdong Qi

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 1294033 (2023) https://doi.org/10.1117/12.3011223
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

An improved model combining convolutional neural network and bidirectional long and short-term memory network is proposed to realize the intelligent diagnosis of GIS device fault. First, the feature vector extraction of the speech data through wavelet packet decomposition, and the results are used as input to the CNN-BiLSTM model to learn the sound feature relationship of GIS devices. Secondly, it improves the ability of the model to intelligently diagnose faults of GIS devices by optimizing parameters and accelerating convergence. After experimental verification, the proposed model has obvious advantages in GIS equipment fault diagnosis over the traditional fault diagnosis methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoliang Zhuang, Qiankun Li, Yanhui Shi, Xueming Huang, Chenjiarui Gong, and Xiangdong Qi "Fault diagnosis of GIS equipment based on voice print recognition and algorithm research", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 1294033 (1 December 2023); https://doi.org/10.1117/12.3011223

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KEYWORDS

Geographic information systems

Instrument modeling

Data modeling

Error analysis

Detection and tracking algorithms

Feature extraction

Wavelet packet decomposition

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