CAJ | 학술논문

为了提取有效的故障特征，提出了基于频率切片小波变换时频分解的故障特征分离提取方法。先对信号进行频率切片小波变换获取其时频分布，然后根据信号的能量分布特点选择时频区域，再以较高的时频分辨率对选择的时频区域进一步细化分析，以突出隐含在信号中的时频特征，在此基础上分割出含有故障特征时频区域，再通过滤波和逆变换重构分离出有效的故障特征。仿真实验和工程应用表明，这种方法可从噪声信号中分离出有效的特征分量，在发电机组故障特征提取时取得了较好的效果。
위료제취유효적고장특정，제출료기우빈솔절편소파변환시빈분해적고장특정분리제취방법。선대신호진행빈솔절편소파변환획취기시빈분포，연후근거신호적능량분포특점선택시빈구역，재이교고적시빈분변솔대선택적시빈구역진일보세화분석，이돌출은함재신호중적시빈특정，재차기출상분할출함유고장특정시빈구역，재통과려파화역변환중구분리출유효적고장특정。방진실험화공정응용표명，저충방법가종조성신호중분리출유효적특정분량，재발전궤조고장특정제취시취득료교호적효과。
In order to extract useful features from vibration signals, a fault feature separation and extraction method using the new time-frequency decomposition method, the frequency slice wavelet transform (FSWT), was proposed. The vibration signal firstly was processed with the FSWT to get its time-frequency distribution. Then zoom analyses were adopted with higher resolutions to reveal the submerged time-frequency features for the selected time-frequency regions by investigation of the time-frequency distribution characteristics. On the basis of the interested regions, the time-frequency features were extracted with making segmentation on the distribution map. Thereafter, after filtering, the effective fault features were separated from the signal through the FSWT reconstruction. The proposed method is shown to be efficient by simulations and engineering applications. It has the ability to isolate the desired components from noisy signals. It achieves an ideal effect on feature extraction for a generator unit fault diagnosis.