lmd

Some research on rotor systems diagnosis based on LMD has been done .

本文对LMD方法在转子系统故障诊断中的应用进行了研究。

Instantaneous frequency extraction method and experiment based LMD

基于LMD的信号瞬时频率求取方法及实验

Elimination of Defects in Nodular cast Iron Gears With LMD Process

用LMD工艺消除球铁齿轮的铸造缺陷

Combine LMD with neural networks , the method based on LMD and neural networks is proposed .

将LMD和神经网络相结合，提出一种基于LMD与神经网络的智能诊断方法。

A novel method of time-frequency analysis , Local Mean Decomposition ( LMD ) is applied to the rotor system fault diagnosis .

本文将时频分析的新方法&局部均值分解法（LocalMeanDecomposition，简称LMD）应用于转子系统的故障诊断中。

LMD Series professional LCD monitor

LMD系列专业液晶监视器

Control of The LMD Lime Kiln

新型LMD石灰窑的运行

This paper introduces how to fix the important running date of LMD lime kiln , and the keys of daily maintenance .

文章介绍了如何确定新型LMD石灰窑的一些重要运行参数以及日常运行维护要点。

LMD 200 ml and urapidil 25 mg be maintained to 2 hours intravenous infusion .

静脉滴注低分子葡聚糖200ml加压宁定25mg，维持2小时。

The experimental results show that the rotor systems fault diagnosis method based on LMD and neural network can identify rotor systems fault patterns accurately and effectively . 2 .

结果表明，基于LMD与神经网络的故障诊断方法能够准确、有效地对转子系统的工作状态和故障类型进行分类。

Combine wavelet packet with LMD to improve the original method that is adopting LMD method to decompose the reconstructed signal after wavelet packet .

将小波包和LMD方法相结合，改进了原来的LMD方法，对小波包重构后的信号再进行LMD分解。

Conclusion : The study demonstrated that intravenous LMD and low dose urapidil produced acute and significant haemodynamic benefit in patients with left heart dysfunction .

结论：本研究表明静脉注射低分子葡聚糖加小剂量压宁定对左心功能不全有急性与明显的效益。

A fault diagnosis approach for rotor systems based on the combination of improved LMD and singular value decomposition is proposed for the frequency confusion of LMD .

针对LMD分解法的频率混淆问题，提出了基于改进的LMD和奇异值分解法相结合的转子系统故障诊断方法。

In all patients , the haemodynamic record was performed before and after 15 minutes , 30 minutes , 1 hour , 2 hours LMD infusion .

在滴注前及滴注后15分钟、30分钟、1小时及2小时进行血流动力学检查。

The main work is done as follows : 1 . A rotor systems fault diagnosis method based on LMD and neural network is proposed to deal with the non-stationary vibration signal from fault rotor systems .

主要研究工作如下：1.针对转子系统故障振动信号的非平稳特性，提出了一种基于LMD和神经网络相结合的故障诊断方法。

By introducing and comparing the already existing mature ETS and combining the Chinese situation analyzed by Logarithmic Mean Weight Divisia ( LMD ) method , we present a tentative concept of ETS in China .

本文通过对成熟排放权交易体系的介绍和比较，再结合对数平均权重分解法分析所得的中国的国情，对中国的温室气体排放权交易体系提出构想。

Objective : It was investigated the haemodynamic effect of low molecular dextran ( LMD ) in left heart dysfunction in due to certificate it 's efficiency of haemodilution and improvement microcirculation to left heart dysfunction .

目的：探讨低分子葡聚糖对左心功能不全的血流动力学改变，以证实其可稀释血液、改善微循环，对左心功能不全的疗效。

Aiming at the non-convergent problem of LMD algorithm , the new selection method of moving averaging span is provided , which can make LMD convergent in the case the distances between neighboring extreme points have great variety .

提出在信号的相邻极值点距离变化比较大的情况下，应用新的滑动平均跨度的选择方法，使LMD算法易于收敛。

Targeting the characterstics that periodic impulses signal often occur when the roller bearing exhibits local faults and the technical defects of the singular value decomposition , a method of feature extraction based on improved LMD and SVD is put forward .

针对滚动轴承出现局部故障时往往伴随着周期性脉冲信号这一特点与奇异值分解技术的缺陷，提出了一种基于改进LMD奇异值分解的故障特征提取方法。

Vibration signal of the roller bearing with fault is analyzed using this method , experimental results show that the method based on SVD and improved LMD and SVMs have a high recognition rate in fault diagnosis , so as provides a new method to roller bearing fault diagnosis .

对滚动轴承实验信号的分析结果表明，基于改进LMD奇异值分解和SVM的故障诊断方法对滚动轴承的故障有很高的识别率，从而为滚动轴承故障诊断提供了一种新方法。

Firstly , we adopt the wavelet technique to decompose the vibration signal of rotor systems into several wavelet components , then each of wavelet component is decomposed by the LMD method and a number of PF components can be acquired from which the initial feature vector matrix is formed .

该方法先用小波包分解法将转子振动信号分解成若干个小波包分量，进一步对各小波包分量进行LMD分解，得到一系列PF分量，形成初始特征向量矩阵。