Dimensionality reduction via variables selection - Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox

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 multi-stage 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.