Smart Feature Extraction from Acoustic Camera Multi sensor Measurements
The paper applies recently developed smart approach for feature extraction from multi dimensional data sets using Echo state networks (ESN) to the focalized spectra obtained from the acoustic camera multi sensor measurements. The aim of the study is development of distance diagnostic system for prediction of wearing out of bearings. The procedure for initial features selection and features extraction from the focalized spectra was developed. Then the k means clustering algorithm and Support vector machine (SVM) classifiers were applied to differentiate the tested bearings into two classes with respect to their condition "Good" or "Bad" The results using different dimensions of the extracted features space were compared.