基于支持向量机的齿轮故障诊断

Shangjun Ma, Geng Liu, Yongqiang Xu
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引用次数: 7

摘要

将支持向量机元素应用于齿轮系统的故障诊断,建立了针对无故障齿轮模式、齿根断裂模式和齿面磨损模式3种单独故障模式的两类算法。通过训练测试仿真数据样本和齿轮振动信号样本,最终在转速为300r/min和900r/min时,识别并区分出这3种不同类型的齿轮故障模式。结果验证了支持向量机在齿轮故障诊断系统中具有良好的诊断能力,在该领域具有良好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gear fault diagnosis based on SVM
Elements of Support Vector Machine was applied to the fault diagnosis of gear system, and the two-class algorithms for 3 individual fault modes, which are No Fault Gear Mode, Crack of Dedendum Mode and Tooth Surface Abrasion Mode respectively, are well developed and set up. Through the training and testing simulation data samples and the signal samples from gear oscillation, these 3 different types of gear fault modes are finally identified and distinguished from each other at the rotating speed of 300r/min and 900r/min. The result validates that the Support Vector Machine is with excellent diagnostic ability in the fault diagnosis system of gear and with favorable prospect in this filed of application.
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