基于递归神经网络的滚动轴承故障预测

Qiangqiang Cui, Zhiheng Li, Jun Yang, Bin Liang
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引用次数: 17

摘要

滚动轴承装置在世界上几乎所有的工业中都得到了广泛的应用,并发挥着非常关键的作用。所以一旦这个关键设备出现故障,整个系统将会受到非常严重的影响。它不仅会影响整个系统的性能,还会影响系统的可靠性、安全性、适用性等。因此,预测轴承失效是非常重要的。由于递归神经网络在处理序列问题方面非常有效,它经常被用于处理与预测相关的问题。近年来,递归神经网络受到了广泛的关注,因此本文选择将递归神经网络用于滚动轴承故障预测。随后,我们使用实际的滚动轴承故障数据来验证我们的方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rolling bearing fault prognosis using recurrent neural network
Rolling bearing devices are widely used in almost all industries in the world, and play a very critical role. So that once this critical device fails, the whole system will have a very serious impact. It will not only affect the performance of the entire system, but also the system reliability, security, applicability and so on. Therefore, it is very important to predict the bearing failure. Because recurrent neural network is quite effective in dealing with sequence problems, it is often used to do prediction-related problems. And in recent years, recurrent neural network has been put into great attention, so here we choose to use RNN for rolling bearing fault prognosis. Afterwards, we use the actual rolling bearing fault data to verify the effectiveness of our method.
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