Features Extraction for Classification in Switching Devices using Fiber Bragg Grating

Abstract Vibration analysis systems are used to assess the operational condition of machines and electromechanical components in various applications. This work presents a measurement and feature extraction system that analyzes dynamic strain patterns in signals measured by fiber Bragg grating (FBG). The features were used to identify different simulated operational conditions in an electromechanical relay. The selection of the best feature space in the first approach was performed by statistical criteria that determine the threshold values and frequency bands to calculate each signal's switching time and power spectral density (PSD). These parameters are used in the support vector machine (SVM) algorithm, which presents 98 % accuracy for distinguishing four distinct conditions. Another methodology for extracting features, called wavelet scattering transform (WST), was used to demonstrate that it is possible to achieve even better performance levels. The results allow extending the methodology to more complex systems.