Rolling Element Bearing Fault Diagnosis Using an Integrated Approach Incorporating Teager-Kaiser Energy Operator and Singular Spectrum Analysis

Purpose The fault induced in the rolling element-bearing components alters its vibration characteristics. Estimating characteristic fault frequencies from vibration signatures leads to a robust assessment of the health state of the bearing. The purpose of the undertaken work is to develop an approach for the detection of faults, through the detection of fault characteristic frequencies, in rolling element bearings incorporating the Singular Spectrum Analysis (SSA) and Teager-Kaiser Energy (TKE) operator. Methods The SSA is a reliable non-parametric method used to separate arbitrary signals from noise having a broad spectrum of applications ranging from biomedical signals to economics. The method consists of mainly two stages: decomposition and reconstruction. The singular value decomposition (SVD) based decomposition process generates a number of singular value components depending upon the energy content. In the traditional approach, SSA emphasizes preserving high-energy singular components for reconstructing signal components from signal noise mixture. This approach has a limitation in identifying weak signal components. For bearing having incipient fault often generates weak fault signals. Hence, a conventional SSA may not be that effective in detecting the incipient faults in the bearing. The accuracy of SSA depends upon the combination of window length L and the number of sub-signals considered for reconstruction r. An approach is proposed to estimate the appropriate decomposition and reconstruction parameters for bearing fault diagnosis. It employs a non-linear Teager-Kaiser Energy (TKE) operator to enhance the impulsive feature of the raw vibration signature by converting it into a Teager-Kaiser (TK) energy signal. Results For a TK energy signal with N data points, an effective combination of window length L = N/2 and reconstruction parameter r = 2 or 3, has been identified to extract the oscillatory component corresponding to characteristic fault frequency for diagnosis. Conclusion The method's accuracy is validated from the large set of real-time vibration signals. The proposed method enhances the robustness of the bearing fault detection as it is in line with the classical fault detection technique aiming at detecting the characteristic fault frequencies.