基于机器学习的旋转机械故障诊断研究

IF 0.2 Q4 ACOUSTICS
H. Jeon, Ji-Sun Kim, Bong-Ju Kim, Won-Jin Kim
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引用次数: 0

摘要

在本研究中,制作了一台能够再现正常情况和8种故障情况的旋转机器,并获取了振动数据。根据采集的数据计算特征,并使用人工神经网络和遗传算法通过故障诊断来分析准确性。为了实现最佳时序和更高的精度,将三个领域的特征应用于故障诊断。选择学习编号作为设置变量。作为旋转机械故障诊断的结果,在频域中发现了比其他领域更高的精度,并且在学习次数为5000和8000的情况下,通过所有10次操作都实现了精确的故障诊断。考虑到时间的效率,估计学习次数为5000次时效率最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study on the fault diagnosis of rotating machine by machine learning
In this study, a rotating machine that can reproduce normal condition and 8 fault conditions were produced, and vibration data was acquired. Feature is calculated from the acquired data, and accuracy is analyzed through fault diagnosis using artificial neural networks and genetic algorithms. In order to achieve optimal timing and higher accuracy, features by three domains were applied to the fault diagnosis. The learning number was selected as a setting variable. As a result of the rotating machine fault diagnosis, high precision was found in the frequency domain than in others, and precise fault diagnoses were accomplished through all of 10 operations, at the learning number of 5000 and 8000. Given the efficiency of time, it was estimated to be the most efficient when the number of learning was 5000.
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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