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首页> 外文期刊>Applied Acoustics >Dimensionality reduction via variables selection - Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox
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Dimensionality reduction via variables selection - Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox

机译：通过变量选择降低尺寸-线性和非线性方法在行星齿轮箱基于振动的状态监测中的应用

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Feature extraction and variable selection are two important issues in monitoring and diagnosing a planetary gearbox. The preparation of data sets for final classification and decision making is usually a multistage process. We consider data from two gearboxes, one in a healthy and the other in a faulty state. First, the gathered raw vibration data in time domain have been segmented and transformed to frequency domain using power spectral density. Next, 15 variables denoting amplitudes of calculated power spectra were extracted; these variables were further examined with respect to their diagnostic ability. We have applied here a novel hybrid approach: all subset search by using multivariate linear regression (MLR) and variables shrinkage by the least absolute selection and shrinkage operator (Lasso) performing a non-linear approach. Both methods gave consistent results and yielded subsets with healthy or faulty diagnostic properties.
机译：特征提取和变量选择是监视和诊断行星齿轮箱的两个重要问题。为最终分类和决策制定数据集通常是一个多阶段的过程。我们考虑来自两个变速箱的数据，一个处于健康状态，另一个处于故障状态。首先，使用功率谱密度将时域中收集的原始振动数据进行分段，并转换为频域。接下来，提取15个变量，这些变量表示计算出的功率谱的幅度；这些变量就其诊断能力进行了进一步检查。我们在这里应用了一种新颖的混合方法：使用多元线性回归（MLR）进行所有子集搜索，并通过执行非线性方法的最小绝对选择和收缩算子（Lasso）进行变量收缩。两种方法均给出一致的结果，并产生具有健康或错误诊断属性的子集。

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