信号处理技术是旋转机械故障诊断的核心。
The core of rotating machinery fault diagnosis technology is signal processing.
提出了基于双谱分析的旋转机械故障诊断新方法。
A new method based on bispectral analysis for rotating machinery faults diagnosis is presented.
旋转机械故障诊断的一个困难问题是诊断规则的获取。
A difficult issue encountered during the diagnosis of rotating machine failures consists in the acquisition of diagnostic rules.
扭转振动监测是旋转机械故障诊断研究领域中的重要内容。
Torsional vibration monitoring plays an important role in the research field of rotating machinery.
提出一种基于粗糙集理论的大型旋转机械故障诊断和知识获取模型。
A model of the fault diagnosis and decision rules acquisition for large rotating machinery based on Rough set is put forward.
轴心轨迹的图形形状识别是旋转机械故障诊断中最为重要的内容之一。
The identification of the graphic shape of core path is one of the most important contents in the failure diagnosis of rotation machinery.
在旋转机械故障诊断模型的基础之上，对风机的常见故障类型作了分析。
On the based of rotating machinery fault diagnosis model, familiar fault on gas blower are analyzed.
在研究数据挖掘技术的基础上，建立了旋转机械故障诊断的特征挖掘模型。
A feature mining model was set up for rotating machine fault diagnosis, based on data mining.
旋转机械故障诊断的研究对于避免灾难性事故和巨额经济损失具有重要的意义。
Research on rotating machinery fault diagnosis is of great significance for avoiding catastrophic accidents and huge economic losses.
以旋转机械故障诊断问题和油藏开发过程采收率的模拟为例验证了算法的有效性。
The effectiveness of the algorithm has been proved in the rotation machinery fault diagnosis and the simulation in oil field development process.
针对旋转机械故障诊断专家系统中的知识表示问题，讨论了语义网络的知识表示方法。
Aiming at knowledge representation problem of expert system for rotary machinery fault diagnosis, semantic net knowledge representation is discussed.
用此表结合神经网络和演化算法进行诊断，开发了一个通用型智能旋转机械故障诊断系统。
A common intelligence fault diagnosis system for the rotating machinery can be developed based on this theory.
工程实践应用表明：短时矢谱分析对于旋转机械故障诊断是一种新的、较为实用的信息融合方法。
Engineering practice indicates that the Short Time Vector-Spectrum is a new and usable method for rotary machinery fault diagnosis.
实践表明，全矢能量谱作为对转子涡动信号处理的能量分析方法，对于旋转机械故障诊断是非常实用的分析工具。
It has been shown that the full vector energy spectrums are a very practical analysis tool to fault diagnosis of rotary machinery as a energy analysis method to process whirl signal of rotors.
为此，研究信息融合技术在旋转机械故障诊断中的应用，降低故障诊断的不确定性，提高设备的诊断精度显得尤为必要。
For decreasing uncertainty and improving accuracy of fault diagnosis, it is very necessarily to research the application of information fusion technology in rotating machinery fault diagnosis.
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 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.
但对于复杂的故障，单一的神经网络诊断很难得出准确结果，考虑到旋转机械故障的复杂性，因而将集成神经网络应用于旋转机械故障诊断中。
But with the complicity of the fault, using the simple neural network is not produced accurate conclusions, the integrated neural networks will overcome the shortcomings.
为了避免经验模式分解(EMD)过程中不同时间尺度函数间的模式混叠，采用基于高斯白噪声加入的经验模式分解方法，并将之应用于旋转机械故障诊断中。
The EMD added Gauss white noise is proposed to avoid mode mixing of different time-scale IMF, and is applied in fault diagnosis for rotating machine.
对实际旋转机械故障振动信号的分析结果表明，该方法能有效避免固有模式函数间模式混叠，提高故障诊断效果。
This method can effectively eliminate mode mixing of IMF and improve the quality of fault diagnosis through analyzing vibrating signal of rotating machine.
对实际旋转机械故障振动信号的分析结果表明，该方法能有效避免固有模式函数间模式混叠，提高故障诊断效果。
This method can effectively eliminate mode mixing of IMF and improve the quality of fault diagnosis through analyzing vibrating signal of rotating machine.
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