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.
磨损规律和机械故障诊断等研究都离不开磨粒分析技术。
The study of wearing rules and mechanical fault diagnoses is based on wear particle analysis.
机械故障诊断和监测在工业生产中占有越来越重要的地位。
Mechanical fault diagnosis and monitoring plays an increasingly important position in industrial production.
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.
机组运行状态的监测与评估一直是机械故障诊断技术研究的关键。
The condition monitoring and evaluation is one of the key techniques in machinery diagnostics.
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.
在机械故障诊断中，特征选择和分类器的参数优化都可以提高诊断精度。
Feature selection and parameters optimization of the fault classifier can enhance the fault diagnosis accuracy.
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.
本文主要论述了机械故障诊断中，几类振动信号时域波形的分析与识别方法。
In this paper, several time domain waveforms of vibration signal and its identification method in mechanical fault diagnosis are analysed.
故障机理的研究振动信号分析是机械故障诊断技术中采用的最主要的方法之一。
Failure mechanism of vibration signal analysis is the mechanical fault diagnosis technology used in one of the main method.
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.
准确地提取各种典型故障的振动信号的时频域特征是进行机械故障诊断的关键。
Extracting precisely the vibration signals' characteristics in time and frequency of typical faults is a key to mechanical failure diagnosis.
机械故障诊断是研究设备运行状态信息的变化，进而识别设备运行状态的科学。
Machine fault diagnosis is a science of identifying the running state of device by studying the varying information of the device running state.
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.
在复杂的振动信号中准确提取出冲击响应信息在机械故障诊断中具有重要的意义。
It is important to exactly extract impulse response information from complex vibration signals for mechanical fault diagnosis.
研究结果表明：基于分形的机械故障诊断方法对复杂机械系统的早期故障更为敏感。
The results show that the method of the mechanical fault diagnosis based on fractal theory is more sensitive to initial fault type of complicated mechanical system.
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.
针对传统的机械故障诊断方法的局限性，提出将人工神经网络应用于机械故障诊断中。
For the limitations of traditional methods in the mechanical fault diagnosis, it is proposed in this paper that the artificial neural network is used in the mechanical fault diagnosis.
传统的机械故障诊断大都以单通道信号分析为基础，从中提取有关机器行为的特征信息。
But the traditional fault diagnosis of machinery is based on the analysis of the signal coming from single channel from which the character information about the machinery action is picked up.
这种方法的提出对内燃机等往复动力机械故障诊断具有重要的理论意义和现实应用价值。
This method has important academic meaning and value of practical application on fault diagnosis in to-and -fro dynamical machine.
高压断路器的绝大部分事故源于机械方面的原因，故研究其机械故障诊断具有重要的意义。
Most faults of circuit breaker is the machinery faults, so study the method of faults diagnose for mechanical parts in circuit breaker is important.
阐述了人工神经网络模型的一般结构和算法，并设计了机械故障诊断神经网络的模型和学习过程。
The structure and algorithm of artificial neural network model were described, and the model and learning-procedure of mechanical fault diagnosis neural network were designed.
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.
在理论和实验研究的基础上，将所提出的基于神经网络的WSN数据融合的方法进行机械故障诊断。
According to the research of theory and experiment, the data aggregation technology based on neural network in WSN proposed in this thesis is applied to machinery fault diagnosis.
转子横向裂纹的早期捕捉问题一直是久久困扰现场、在机械故障诊断领域亟待解决的关键技术之一。
The early capturing technique of lateral crack faults is one of the most important techniques to be tackled.
转子横向裂纹的早期捕捉问题一直是久久困扰现场、在机械故障诊断领域亟待解决的关键技术之一。
The early capturing technique of lateral crack faults is one of the most important techniques to be tackled.
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