【摘要】：故障信號特征提取的準確性是保證故障智能診斷識別率的關(guān)鍵因素。低信噪比情況下,故障診斷效果下降。變分模態(tài)分解方法(VMD)在信號分解精度和抗噪方面具有明顯優(yōu)勢。在分析VMD抗噪性能的基礎(chǔ)上,提出以VMD分解的各模態(tài)能量作為智能診斷特征信息,并與小波包的特征信息進行對比研究。將滾動軸承兩種故障特征信息通過BP神經(jīng)網(wǎng)絡(luò)識別,用不同信噪比的加噪故障信號進行測試,結(jié)果表明,在低信噪比情況下基于VMD模態(tài)能量的故障特征更具有可識別性。
[Abstract]:The accuracy of fault signal feature extraction is the key factor to ensure the recognition rate of intelligent fault diagnosis. Under the condition of low signal-to-noise ratio (SNR), the effect of fault diagnosis is decreased. (VMD) has obvious advantages in signal decomposition accuracy and anti-noise. On the basis of analyzing the anti-noise performance of VMD, the modal energy of VMD decomposition is proposed as the characteristic information of intelligent diagnosis, and the characteristic information of wavelet packet is compared with that of wavelet packet. Two kinds of fault feature information of rolling bearing are identified by BP neural network and tested with different signal-to-noise ratio (SNR). The results show that the fault feature based on VMD modal energy is more identifiable in the case of low SNR.
【作者單位】： 上海開放大學(xué)信息與工程學(xué)院;上海大學(xué)機電工程與自動化學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(51575331)
【分類號】：TH133.33

【相似文獻】

相關(guān)博士學(xué)位論文 前1條

1 呂中亮;基于變分模態(tài)分解與優(yōu)化多核支持向量機的旋轉(zhuǎn)機械早期故障診斷方法研究[D];重慶大學(xué);2016年

相關(guān)碩士學(xué)位論文 前2條

1 王敬;基于離散模態(tài)分解法的表面建模與裝配仿真研究[D];浙江大學(xué);2017年

2 王振威;基于變分模態(tài)分解的故障診斷方法研究[D];燕山大學(xué);2015年

，

本文編號：2492369