Open AccessGIS Mechanical Fault Diagnosis Method Based on Middle Time Mel Cepstrum Coefficient
Author(s) -
Yi Jiang,
Min Xu,
Rui Lin,
Lezhou Hong,
Guosheng Lu,
Zhe Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1659/1/012055
Subject(s) - mel frequency cepstrum , fault (geology) , cepstrum , support vector machine , switchgear , computer science , pattern recognition (psychology) , artificial intelligence , speech recognition , engineering , feature extraction , geology , mechanical engineering , seismology
Mechanical fault is a common fault of gas insulated switchgear (GIS). If not found in time, it will cause major safety hazards such as opening and closing fault. In this paper, a diagnosis method based on improved Mel cepstrum coefficient for GIS mechanical fault on-line monitoring is proposed. Firstly, the Mel frequency cepstral coefficients (MFCC) are extracted from the preprocessed sound signals; in order to adapt to the characteristics of smooth sound energy change in GIS operation, the MFCC is optimized to obtain improved features; Support Vector Machine (SVM) is introduced to build a GIS mechanical fault diagnosis model based on acoustics and uses bagging algorithm to integrate the SVM model. In this study, mechanical fault is simulated on real GIS to obtain real fault sound signals for training and testing. The experimental results show that compared with the traditional MFCC, the improved MFCC has a higher recognition accuracy in GIS fault sound recognition system. The F1-score is generally over 80%, and the F1-score of specific mechanisms can even reach 99%.