Multicriteria Gear Monitoring System Based on Deep Neural Networks

被引：0

作者：

Lai, Chia-Hung ^{[1
]}

Wu, Ting-En ^{[2
]}

机构：

[1] Natl Chin Yi Univ Technol, Dept Intelligent Automat Engn, 57,Sect 2,Zhongshan Rd, Taichung 411030, Taiwan

[2] Natl Changhua Univ Educ, Dept Ind Educ & Technol, 2 Shi Da Rd, Changhua 50074, Taiwan

来源：

SENSORS AND MATERIALS | 2023年 / 35卷 / 12期

关键词：

gear wear; vibration; condition monitoring; TIME-FREQUENCY ANALYSIS; FAULT-DIAGNOSIS;

D O I：

10.18494/SAM4709

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

Gears are commonly used mechanical components in various fields of power transmission. They offer stability and high transmission efficiency. However, the issue of gear lifespan remains unavoidable. In this study, we have developed a gear monitoring system that employs deep neural networks (DNNs) and integrates data from the time domain, frequency domain, shorttime Fourier transform (STFT), and discrete wavelet transform (DWT). This system is designed to monitor the occurrence of wear in both spur and helical gears. In this research, we expand its practical applications, implement various gear fault detection methods based on deep neural networks, provide multicriteria for gear monitoring, and offer experimental results demonstrating its effectiveness.

引用

页码：4481 / 4489

页数：9

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