随后以故障树风险分析理论为基础，对故障事件进行建模和定性定量分析，确定表征设备故障特征的信息，提取关键因素。
Furthermore, fault events are modeled and made into analysis based on the risk analysis theory of fault tree. After that equipment failure characters are confirmed and key factors are exacted.
电力设备的故障诊断可以看成一个模式分类问题，每一个电力设备故障对应一组特征集。
The fault diagnosis of power equipment can be looked as pattern classification problem. Each fault of power equipment is corresponding to a set of traits.
多层前馈人工神经网络在装备故障诊断中的应用含设备运行状态特征值设定和故障判定。
The application of multi-layer feed-forward artificial neural network in fault equipment diagnosis includes feature value setting of equipment operation condition and fault judgment.
利用人工神经网络理论，通过对设备振动信号采集、处理和提取特征参数的方法，对装载机机械系统工作状态进行智能监测与故障诊断。
This paper involves ANN based intelligent condition monitoring and diagnosing of loaders, focusing on signal collecting and processing as well as characteristic parameter picking up.
上述的各种方法都要基于完整的设备参数和特征参数的数据才能实现对数控机床主轴箱的故障诊断，所以一个全面完备的故障诊断数据库是必不可少的。
The basis of methods above is integrated data of equipment parameter and character, so a self-contained and complete database of fault diagnosis is mostly necessary.
在机电设备的故障诊断中，特征提取是最重要也是最困难的一个环节。
The feature extracting is an important, difficult step in the fault diagnosis processing.
按油中溶解的特征气体含量数据与注意值比较进行判断，可以对变压器等设备有无故障及故障性质作出初步判断。
According to the compare of the actual data and the alarm value of the dissolved gas in the oil, we can make whether there is failure and its primary characteristic for the transformer.
本发明方法、系统和集成装置可以快速、方便、灵敏地测定SF 6电气设备中的特征故障气体。
The method, a system and an integrated device can quickly, conveniently and sensitively measure the characteristic fault gas in the SF6 electrical equipment.
有效特征向量的提取和状态识别是设备状态监测与故障诊断领域中的关键技术。
The extraction of effective feature vectors and pattern recognition are the key technique in the subject of mechanical condition monitoring and diagnosis.
有效特征向量的提取和状态识别是设备状态监测与故障诊断中的关键技术。
The extraction of effective feature vector and pattern recognition is the key technique in the process of mechanical condition monitoring and diagnosis.
大型电力变压器局部放电信号的特征提取是电气设备绝缘在线监测及故障诊断技术领域的前沿研究课题。
The feature extraction of partial discharge signal of transformer is the front research project in online monitoring and diagnosing high voltage insulation failure.
信号处理是提取故障特征信息的主要手段，而故障特征信息则是进一步诊断设备故障原因并采取防治对策的依据。
Signal Processing is the main method to extract fault feature information. These information become the base for further way to diagnose machine fault and take measures to prevent machine fault.
在电子设备故障诊断中，波形识别是进行故障特征提取和故障诊断的重要依据。
In electronic equipment fault diagnosis, waveform identification is an important basis of features extraction and fault diagnosis.
在电子设备故障诊断中，波形识别是进行故障特征提取和故障诊断的重要依据。
In electronic equipment fault diagnosis, waveform identification is an important basis of features extraction and fault diagnosis.
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