Vibration Feature Extraction Using Audio Spectrum Analyzer Based Machine Learning

Jyun-Shun Liang, Kerwin Wang
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引用次数: 7

Abstract

To develop a tool to identify different mechanical vibrations, this paper presents a novel instrument to extract the vibration features of rotating machinery. The instrument consists of an audio spectrum analyzer, a signal processing circuit and a single-board computer. This architecture offers a cost-effective solution for machine status monitoring and analyzing. The experiment results show that the training data collected from audio spectrum analyzer can work well with both KNN and SVM methods to construct accurate machine-learning models with the 95.8% and 97.2% accuracy respectively.
基于机器学习的音频频谱分析仪振动特征提取
为了开发一种识别不同机械振动的工具,本文提出了一种提取旋转机械振动特征的新型仪器。该仪器由音频频谱分析仪、信号处理电路和单板计算机组成。该体系结构为机器状态监测和分析提供了一种经济有效的解决方案。实验结果表明,利用音频频谱分析仪的训练数据,KNN和SVM方法均能较好地构建准确的机器学习模型,准确率分别为95.8%和97.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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