特征提取使用诊断功能设计应用程序
在这个视频中,我们将演示如何使用诊断功能设计应用为发展预见性维护算法提取特征。
我们开始通过导入数据集应用。在收集数据在不同故障条件下从三缸泵。这是存储在一个专门的数据存储为发展预见性维护算法。整体数据存储包含1.2秒长测量的流量和压力,也为每一个测量故障代码。导入后的数据集,它出现在数据浏览器。可视化流信号,我们选择它并单击信号跟踪。这情节不同故障条件下的测量。我们现在可以测量的故障代码通过选择此选项。如果我们放大使用下面的淘洗沙金的人带,我们可以更好的看到测量突出显示不同的颜色根据不同的故障类型。接下来,我们将从这些数据中提取时域和频谱特性。我们回到特性设计师标签,这个菜单下,我们选择信号特性生成统计特性。 We’ll first use the flow data and later extract features from the pressure signal. Here, we have commonly used time-domain features such as the mean, standard deviation, kurtosis, and skewness. Now that we computed the time-domain features, we’ll continue with extracting spectral features. The app can use the time-domain data to estimate the signal spectra of these signals which can be then used to extract spectral features. We select spectral estimation and click power spectrum. Here, you can try out nonparametric or parametric methods to compute the spectrum and compare their results. We choose the auto-regressive model with a model order of 20. Next, to compute spectral features, we click here. We select the frequency band such that it includes the first four peaks. The reason is that due to noisy data at higher frequencies, it’s harder to distinguish the spectral peaks. Therefore, any features extracted from higher frequencies won’t contribute to the performance of machine learning models.
到目前为止,我们确定时间和光谱特性的流数据。您可以重复相同的过程与压力数据和提取一些额外的特性。现在,所有提取的特征从流量和压力数据存储在FeatureTable1。选择此表后,我们可以按一下功能表视图显示所有计算特征值以表格形式。不同的功能是显示在不同的列。我们也可以用柱状图显示分布的计算功能。在这些情节,不同的颜色表示不同的缺点。由于重叠分布不同的故障类型和大量的特性,很难决定哪些特性更可分和与众不同。应用程序让我们排名的所有特性识别的有效分离不同类型的错误。功能设计选项卡上,当我们点击排名功能,应用程序使用单向方差分析来计算排名分数的所有特性。 The results of the ANOVA test are displayed on the right-hand side, whereas the bars on the left shows the normalized scores for different features. We can view the feature names by hovering over the bars. The features with a higher score are good candidates for training a machine learning model. For further evaluation of the extracted features, we can now export them to the Classification Learner, where we can train machine learning models for fault classification.
