当前位置: X-MOL 学术Math. Biosci. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A rolling bearing fault detection method based on compressed sensing and a neural network
Mathematical Biosciences and Engineering ( IF 2.6 ) Pub Date : 2020-09-03 , DOI: 10.3934/mbe.2020313
Lu Lu , , Jiyou Fei , Ling Yu , Yu Yuan , , ,

The high sampling frequency of traditional Nyquist sampling theory not only puts greater requirements on the sampling equipment, but also generates a large amount of data, which increases the difficulty of information transmission and storage. To this end, this paper proposes a rolling bearing fault signal detection method based on compressed sensing combined with a neural network. Based on the theory of compressed sensing, the observations obtained from compression sampling are divided into two sets of data. Given the one set of data, the predictive ability of the nonlinear time series through the neural network can predict the second set of observed values. The predicted observations are used to reconstruct the signal, thereby reducing the amount of data to be stored and transmitted and realizing secondary compression of the signal.

中文翻译:

基于压缩感知和神经网络的滚动轴承故障检测方法

传统的奈奎斯特采样理论采样频率高,不仅对采样设备提出了更高的要求,而且产生了大量的数据,增加了信息传输和存储的难度。为此,本文提出了一种基于压缩感知与神经网络相结合的滚动轴承故障信号检测方法。根据压缩感测理论,将从压缩采样中获得的观测结果分为两组数据。给定一组数据,通过神经网络的非线性时间序列的预测能力可以预测第二组观测值。预测的观测值用于重建信号,从而减少要存储和传输的数据量,并实现信号的二次压缩。
更新日期:2020-09-03
down
wechat
bug