Compared with electrical or mechanic system, hydraulic system have the disadvantages of high fault frequency and difficulties to diagnosis, which have greatly effected battle effectiveness in warfare.
与电控及机械系统相比，液压系统故障率高、故障检测定位困难，已成为影响武器装备战斗力发挥的重要因素之一。
In a drive system of power transistor inverter-motor, not only can it be used in fine control of motor current waveform, phase, amplitude and frequency, but can also be used to sense, fault current.
在大功率晶体管逆变器——电动机驱动系统中，它不但可用来对电动机电流的波形、相位、幅值和频率进行精细控制，而且能快速地检测出故障电流。
When disconnected with external power grid due to certain fault, it will result in frequency collapse, and even leads to large-scale blackout owing to the active power imbalance.
当故障导致供电系统与外电网断开后，此时供电系统因有功不平衡，将出现频率崩溃现象，导致大面积停电事故。
Then the time domain analysis methods and frequency domain analysis methods which are frequently used for fault diagnosis have been discussed.
其次论述了常用的故障诊断方法，即时域分析方法和频域分析方法。
The underdeveloped logistics causes a large number of vehicles to stay indoors, which greatly reduce the frequency of a vehicle fault.
物流业的疲软导致大量车辆足不出户，大大降低了车辆的故障发生频率；
Then, make monitoring on ordinary trouble, and frequency spectrum analysis on all kinds of lubricants, and analyses mechanism of happened faults to determine fault causes.
然后对常见故障进行监测，主要对各种润滑油脂进行频谱分析，分析故障发生的机理，确定故障的因果关系。
Frequency information plays an important role in vibration monitoring and fault diagnosis of rotating machinery.
相位信息在旋转机械的振动监测与故障诊断中起着重要的作用。
Considering the power plant condition and vibration frequency, hierarchical diagnostic strategy is put forward. The accuracy of fault diagnosis is improved.
根据机组的状态和振动频率，提出了分阶段、分层次的诊断策略，提高了故障诊断的准确率。
The fault is diagnosed by extracting the fault feature component from the frequency spectrum of the modulated signal.
通过对调制信号作谱分析，以其频谱中是否存在某种故障的特征频率分量来诊断有无该种故障。
Due to high temporal and frequency resolution and high energy focus, the accuracy of fault signal feature extraction is greatly improved by WVD.
WVD很高的能量聚集性和很好的时频分辨率，极大地提高了故障信号特征提取的准确度。
The application shows that it can analyze the change of the short-time energy of vector signals as frequency and time change, being applicable to the fault diagnosis of rotational machinery.
研究表明，短时矢功率谱可以对矢量信号的短时能量随频率、时间等的变化过程作出分析，可以应用于旋转机械故障诊断实践中。
The relation of wear and vibration of gear of simulating pitting fault was studied by using ferrographic analysis and the time and frequency spectrums of vibration analysis technology.
利用铁谱分析技术和振动分析技术中的时频分析方法研究了模拟点蚀故障齿轮磨损与振动的关系。
The expression and frequency variety law of the rotor broken bar fault feature component in the startup electromagnetic torque signal are analyzed in detail.
分析了转子断条故障特征在电机启动电磁转矩信号中的表现形式及频率变化规律。
A novel method of pattern recognition and fault diagnosis in electrical machine based on the wavelet-neural network is proposed according to the frequency spectrum characteristics of vibration signal.
针对电机振动信号的频谱特点，提出基于小波神经网络技术的电机故障模式识别与诊断的新方法。
When the model is connected with the analytic database of signal, the fault parameters corresponding with the characteristic frequency can be gained.
模型与信号分析数据库连接后，可得到该特征频率处的故障参数信息。
When frequency converter and PLC is at fault, automatic switch to the original soft start work frequency power operation, ark to ensure the normal work of the compressor.
当变频器及PLC发生故障时，自动切换到原软启动柜工频电源运行，以保证压缩机的正常工作。
Wavelet packets with the advantage of time frequency zoom is used in this paper to decompose the post fault transient electrical quantities by the proper frequency span.
应用在时频空间皆具有良好聚焦特性的小波包，以适当频率带宽，对故障后暂态电气量进行分解。
Having good time-frequency localization character, and correctly identifying singularity point in fault signal, Wavelet is the main analysis method in this paper.
小波变换具有良好的时频局部性，具有变焦距的特点，对故障信号中的奇异点能够准确识别，是论文中应用的主要分析方法。
As wavelet module maxima occur at peaks of signal, the pretreatment removes power frequency periodic components from electric signal and thus avoids misjudgments of fault.
通过预处理滤除工频周期分量，消除了小波变换在信号峰值处的模极大值，从而避免了对故障的误判。
According to the result of spectrum analysis of vibration signals, frequency characteristics are acquired and various fault signs are gained.
根据振动信号频谱分析结果，提取频谱特征，获取各种故障征兆信息。
Line grounding fault is the most frequency failure for distribution network, and is also the major problem that distribution network operation is faced with.
线路接地故障是配电网最频发的故障，也是配网运行面临的主要问题。
This paper mainly introduces that the fault vibrating signal of gears was decomposed into time-frequency domains by double-orthogonal wavelet analysis and the fault feature of gears was picked up.
利用双正交小波基将齿轮的故障振动信号分解到时频域，并提取出齿轮的故障特征。
By digital simulation and detection of gearbox fault signal the detection effect of the novel bilinear time-frequency transform is validated for the transient components in complex signal.
通过数字模拟实验与齿轮箱故障信号检测，验证了新的双线性时间-频率分布对复杂信号中瞬时分量的探测效果。
The reasons of low detection accuracy are analyzed in detail and the improved methods are proposed. A fault detection approach based on frequency distance comparison is presented.
通过对造成电动机断条故障检测准确度不高的各种原因进行深入的分析，针对这些原因提出了具体的改进方法，并提出了一种基于频距比较的故障检测新方法。
A random resonance self-adaptive algorithm is stated and used for fault signal detection in medium frequency power supply with good result.
提出了一种随机共振自适应算法，并运用于中频电源故障信号检测，取得了较好的效果。
After analyzing current fault diagnosis methods based on multi-frequency sensitivity in analog circuits, a new sensitivity matrix analysis approach is presented to select testing frequency.
在分析现有模拟电路多频灵敏度故障诊断方法的基础上，提出一种新的测试频率范围的选择方法，即灵敏度矩阵分析方法。
In the view of feature signal extraction, the local wave time-frequency analysis and information entropy were used to deal with fault diagnosis.
从特征信号提取的角度出发，采用局域波时频分析和信息熵结合的方法进行往复式压缩机故障诊断。
The fault characteristic periodic signals of the rolling bearing are in low frequency band and often buried in the noise.
反映滚动轴承故障的特征周期信号处于较低频带内，容易被噪声淹没，难以检测。
The early fault signal is very faint, low frequency appears more to pulse, easy to be interfered by the ambient noise even submerged.
早期故障信号非常微弱，多呈现低频脉动，容易受到噪声干扰甚至被环境噪声所淹没。
The character data of the probably fault and machine is acquired by analyzing mechanical fault characteristic frequency and sets configuration.
通过机械故障特征频率分析和机组组态，对可能存在的故障类别和机组类别的特征数据进行提取；
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