Bearing Fault Detection through Vibration and Current Signals decomposed with EMD and SSA

This paper aims to compare the use of different signals, time-series decomposition methods, and classification algorithms for fault detection in rolling bearings. To reach this objective, time series extracted from vibration and electrical current data from real bearings were analyzed. Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA) were used to decompose the time series, and features were extracted from the components and the original series using descriptive statistics. Then Support Vector Machines (SVM), Random Forest (RF), and a Neural Network (NN) were used to detect the faults. The results indicate that decomposing the time series can improve fault detection accuracy and that vibration signals yield better results. The highest accuracy (99.3%) was obtained in the combination of SSA decomposition with the NN classifier.