基于FFT和LLTSA的传动系统故障诊断研究

Fault diagnosis of transmission system based on FFT and LLTSA

在传统的故障诊断方法中,往往先要基于先验知识求取原始振动信号的特征,并将其输入到智能分类器中进行模式识别,其中容易出现信息丢失,且依靠人为经验进行判断不够准确,针对这一问题,提出了基于快速傅里叶变换(FFT)与流行学习联合的智能故障诊断模型。首先,采用FFT变换将原始数据从时域转换到频域,获得了高维特征数据;然后,使用3种流形学习算法,即多维尺度变换(MDS)、核主成分分析(KPCA)、线性局部切空间排列(LLTSA),获得了低维表征信息;最后,基于故障诊断试验平台系统,对轴承及齿轮工作数据信息进行了获取与处理,将其数据样本输入到智能分类器中,进行了训练和测试。研究结果表明:FFT降维变换可以有效地减少人为选择因素引起的样本衰减现象,同时最近邻域估计算法可以提高智能分类器的测试准确率,使得基于FFT与流行学习的联合智能分类模型对状态数据识别率在80%以上,其中FFT+LLTSA联合智能分类模型的识别率最高可达到87%以上;该结果可验证该分类模型在机械传动系统故障检测中具有的有效性。

In the traditional fault diagnosis methods,it was often necessary to obtain the characteristics of the original vibration signal based on a priori knowledge and input it into the intelligent classifier for pattern recognition;it was easy to lose information and the judgment based on human experience was not accurate enough,aiming at the problem,an intelligent fault diagnosis model based on fast Fourier transform(FFT)and popular learning was proposed.Firstly,FFT transform was used to convert the original data from time domain to frequency domain to obtain high-dimensional feature data.Then,three manifold learning algorithms:multi-dimensional scaling(MDS),kernel principal component analysis(KPCA),and linear local tangent space arrangement algorithm(LLTSA),were used to obtain informative low-dimensional representations.Finally,based on the fault diagnosis test platform system,the working data information of bearing and gear was obtained and processed,and the data samples were input into the intelligent classifier for training and testing.The research results show that the FFT dimension reduction transform can effectively reduce the sample attenuation caused by human selection factors,the nearest neighbor estimation algorithm improves the test accuracy of the intelligent classifier,and the intelligent classification model based on FFT and popular learning can identify more than 80%of the state data,among which the highest recognition rate of the FFT+LLTSA joint intelligent classification model can reach more than 87%,which shows the effectiveness of the classification model in the fault detection of mechanical transmission systems.