Statistical Feature Extraction in Machine Fault Detection using Vibration Signal

被引：0

作者：

Van Bui ^{[1
]}

Van Hoa Nguyen ^{[1
]}

Huy Nguyen ^{[1
]}

Fang, Yeong Min ^{[1
]}

机构：

[1] Kookmin Univ, Dept Elect Engn, Seoul 02707, South Korea

来源：

11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020) | 2020年

关键词：

Machine Learning; ANN; LR; SVM; Vibration Signal;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Gearbox faults are one of the most common types in the industrial factory environment. Early detection of these faults allows fast replacement rather than a costly emergency. Nowadays, early machine fault detection application is improving due to the improvement of the IoT network and real-time analysis. The vibration signal is collected from Spectra Quest's Gearbox Prognostics Simulator and analyzed for fault classification. The preprocessing includes fast Fourier transform and statistical feature extraction. The AI algorithms are Artificial Neural Network, Logistic Regression, and Support Vector Machine. The highest accuracy reached is 100%.

引用

页码：666 / 669

页数：4

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