故障特征

最终成功提取出若干能最大限度反映故障特征的特征值，作为神经网络的输入。

At last several eigen values which can Maximum reflect fault signature are successfully picked up , as the input of neural network .

利用信息论中自信息和熵的概念，给出了故障特征信息量的定义，介绍了故障特征信息量的计算方法。

The fault signature information content is defined using the concepts of self-information and entropy in the information theory , and the algorithm of the fault signature information content is presented .

SF6电气设备故障特征气体快速检测方法

Fast Examination Method of Breakdown Characteristic Gases for SF_6 Electrical Equipment

一种是基于Hilbert边际谱的故障特征频率提取的故障诊断方法。

One method is the extraction of fault feature frequency based on Hilbert margin spectrum .

基于Bootstrap方法构造故障特征库的研究

Research of Constructing Knowledge Base Based on Bootstrap Method

基于SVD方法的弱故障特征提取方法

A New Method for Extracting the Weak Fault Symptoms of Current Signal via SVD

基于CCA和SOFM的轴承故障特征提取

Fault Feature Extraction of Bearings Based on CCA and SOFM

对每一个IMF分量建立AR模型，取模型的自回归参数和残差的方差作为故障特征向量，并以此作为输入来建立支持向量机分类器，判断转子系统的工作状态和故障类型。

Then , the support vector machines used as fault classifiers are established to identify the condition and fault pattern of the rotor system .

阻性泄漏电流基波和3次分量是反映MOA劣化现象的两个故障特征量，仿真分析对此问题及间谐波电压对阻性泄漏电流的影响。

Resistive first-harmonic current and third-harmonic current are the two fault eigenvectors reflecting MOA 's aging phenomenon .

该方法使用EMD分离高频调制信号，然后利用基于峭度的自适应形态滤波器进行解调，提取非平稳信号故障特征。

The high frequency modulation signal was separated by using EMD , and it was demodulated by kurtosis-based morphological filtering . Finally , fault features of non-stationary signals were extracted .

研究了基于短时AR分析、小波多分辨率分析和小波包分析的故障特征提取和识别方法，分析了柴油机气缸盖振动信号特征提取方法。

This paper studied the trouble characteristic extraction and identification methods based on snatch AR analysis , wavelet many resolutions analysis and wavelet packet analysis , and analyzed vibration signal characteristic extraction method for diesel engine cylinder head .

最后对实验的数据采用了1（1/2）维谱与小波分析相结合的方法进行故障特征提取，BP网络识别的结果表明，采用这种方法进行滚动轴承故障诊断，结果准确。

Finally , 1 ( 1 / 2 ) dimension spectrum combine with wavelet packets are be used to extract fault feature of rolling bearing . The results of BP network show that this method accurate and reliable in rolling bearing fault diagnosis .

提出了一种以扩展Park矢量方法为故障特征提取手段、利用BP网络的模式识别功能自动诊断异步电机转子断条故障的新方法。

A new method for automatically diagnosing broken-bar fault of rotor of asynchronous motor was proposed in the paper , which taking extended Park 's vector approach to pick up the fault ( characte ) - ( ristics ) and using BP network to recognize the mode of fault .

不断观察模型的平方预测误差（SPE），一旦发现异常，利用主元分析方法，求出当前数据的特征方向，并与故障特征方向库进行比较，从而诊断出故障来。

Squared Prediction Error ( SPE ) of model is detected continuously , in case abnormity is found , currently data direction is calculated by PCA method , and compared with fault character direction storeroom , thereby fault is diagnosed .

这种方法中，局部损伤滚动轴承产生的高频调幅信号成分被EMD分解作为本征模函数分离出来，然后用Hilbert变换得到其包络信号，计算包络谱，就能够提取滚动轴承故障特征频率。

In this approach , the characteristic high-frequency signal with amplitude modulation of a rolling bearing with local damage is separated from the mechanical vibration signal as an Intrinsic Mode Function ( IMF ) by using EMD , and an envelope signal can be obtained by using Hilbert transform .

不同使用因素对HC排放影响显著性不同，并且同一使用因素对HC排放影响在不同的工况下表现程度也有所不一致，即存在故障特征工况。

At the same time , the result showed that HC always changed with the same use factor to different extents in different working conditions , that is to say , there was fault-characteristic working condition caused by the effects of use factors on HC and CO exhaust of engine .

重点讨论了DDVS与Volterra级数的关系，分析了DDVS在参数估计中的数值稳定性和故障特征聚类性；提出了基于DDVS参数估计的故障特征提取方法。

Discussion has been done on relationship between DDVS and Volterra series , and DDVS stability in parameter estimation and fault information capability have been analyzed . Then fault characteristics extracting method based on DDVS parameter estimation was represented .

自适应冗余第2代小波设计及齿轮箱故障特征提取

Adaptive Redundant Second Generation Wavelet Design and Gearbox Fault Feature Extraction

小波包分析法在加速度计模数转换电路故障特征提取中的应用

Fault Character Extraction in Accelerometer ADC Circuit Using Wavelet Packet Analysis

输电线路暂态电压行波的故障特征及其小波分析

Fault Characteristics and Wavelets Analysis of the Transient Voltage Travelling Waves

机械故障特征提取分析及其维修决策

Extraction and Analysis of Mechanical Faults Features and Its Maintenance Decision

基于提升方法的小波构造及早期故障特征提取

Wavelet Construction Based on Lifting Scheme and Incipient Fault Feature Extraction

核函数主元分析及其在故障特征提取中的应用

Kernel Principal Component Analysis and Its Application to Fault Feature Extraction

基于数学形态滤波的齿轮故障特征提取方法

Approach to extracting gear fault feature based on mathematical morphological filtering

雷击电视机故障特征初探

Discussion on the Common Breakdown Features of the Thunderstruck TV Set

发动机转子系统早期故障特征提取方法

Approach in early fault features extraction of aero-engine rotor system

电机故障特征值的倍频小波分析

Multiple bandwidth wavelet analysis for fault diagnosis eigenvalue in squirrel-cage induction motor

大型旋转机械振动现场测试与故障特征分析

Vibration Measurement and Fault Analysis of Large Scale Rotating Machinery

粗糙主成分分析在齿轮故障特征提取中的应用

Rough Principal Component Analysis and Its Application on Feature Extraction for Gears

树型分支电网断线故障特征

The Fault Feature of Line Break for Radial Distribution Line