鉴定(生物学)
断层(地质)
计算机科学
Mel倒谱
模式识别(心理学)
人工智能
类型(生物学)
语音识别
特征提取
地质学
古生物学
植物
地震学
生物
作者
Wang Cha,Haotian Zheng,Qing Yin,Xin Yi
出处
期刊:Journal of physics
[IOP Publishing]
日期:2024-05-01
卷期号:2770 (1): 012028-012028
标识
DOI:10.1088/1742-6596/2770/1/012028
摘要
Abstract As a critical component of the power system, the secure and reliable functioning of electrical equipment is the key to ensuring the dependability of energy supply. In this article, a fault type identification method based on Mel-Frequency Cepstral Coefficients-based Convolutional Neural Networks (MFCC-CNN) for electrical equipment was proposed. Firstly, the reasons for the sound generated by electrical equipment during the operation were analyzed. Then the MFCC coefficient of sound features was extracted by collecting and processing the faulty sound of electrical equipment. Finally, a CNN model was established to train and recognize sound signals. This method combines the recognition and classification of sound with deep learning (DL), which can significantly improve the efficiency of fault diagnosis.
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