| 1. | De - noising of ecg signal based on stationary wavelet transform
基于小波变换的脑电特征信号自动检测方法 |
| 2. | Research on a extraction method for weak fault signal and its application
一种弱故障特征信号的提取方法及其应用研究 |
| 3. | At present , usual method of fd is measuring vibration signal as system feature signals for fault characteristic eigenvectors pickups
故障诊断常用的方法是以泵缸体上的振动信号作为系统特征信号来提取故障特征向量。 |
| 4. | What ' s more , it is not easy to be disturbed by outside environment for requiring the signals from the inside of reciprocating pump cylinder
此方法的优点在于特征信号取自于阀箱内的压力，不易受到外部环境的干扰，适用于多个泵阀同时发生故障的情形。 |
| 5. | Adopting the method of time domain and frequency range , the breakdown characteristic signal is analyzed , and the information of the abnormal state of valve clearance is obtained
并采用时域，频域的分析方法对故障特征信号进行分析，从而得到气门间隙异常状态的信息。 |
| 6. | After the event - by - event charged particle ratio fluctuations were proposed by s . jeori , v . kock et al . as a signature of qgp formation , it had evoked extensive interesting theoretically and experimentally
Kock 、 m人sakawa等人提出荷电粒子比的单事例起伏可以作为qgp形成的特征信号后，引起了理论和实验物理学家的） ’ “泛兴趣。 |
| 7. | The corrosion current and impressed against - corrosion current of a ship modulated by shaft ' s rotation will generate an extremely low frequency electric field ( elfe ) which becomes a kind of important characteristic signal in sea water
摘要舰船的腐蚀和防腐电流经螺旋桨转动的调制后在海水中会?生极低频电场，成?一种重要的特征信号源。 |
| 8. | The character of boiler combustion system and the causes of the faults are analyzed in detail . character signals which shows the status of boiler is extracted and the rule base for fault diagnosis is established
首先详细分析了锅炉燃烧系统故障的特点和产生这些故障的原因，并提取出能反映系统状态的诊断特征信号，建立故障诊断规则库。 |
| 9. | 3 ) based on the idea that the process information is driven by a few of components as independent as possible , a novel process monitoring method is provided whose effectiveness is verified by the research results
3 )根据过程信息能够用若干“尽可能独立”的过程特征信号进行描述的原理，提出了一种基于独立成分分析的过程监控方法。仿真研究表明，这种方法是有效的。 |
| 10. | As we know , feature extraction is the most important and difficult topic in the field of mechanical fault diagnosis . to some extent , feature extraction is a problem which hinder the mechanical fault diagnosis technique from getting further improvement
在机械故障诊断的发展过程中，最重要、最关键、也是最困难的问题之一就是故障特征信号的特征提取，从某种意义上说，特征提取是当前机械故障诊断研究中的“瓶颈”问题。 |
