Bearing diagnostics using image processing methods

In complex machines, the failure signs of an early bearing damage are weak compared to other sources of excitations (e.g. gears, shafts, rotors, etc.). The task of emphasizing the failure signs is complicated by the fact that changes in operating conditions influence vibrations sources and change the frequency and amplitude characteristics of the signal, making it non-stationary. As a result, a joint time-frequency representation is required. Previous vibration based diagnostic techniques focused on either the time domain or the frequency domain. The proposed method suggests a different solution that applies image processing techniques to time-frequency or RPM-order representations (TFR) of the vibration signals in the orders-RPM domain. In the first stage, TFRs of healthy machines are used to create a baseline. The TFRs can be obtained using various methods (Wigner-Ville, wavelets, STFT, etc). In the next stage, the distance TFR between the inspected recording and the baseline is computed. In the third stage, the distance TFR is analyzed using ridge tracking and other image processing algorithms. In the fourth stage, the relations between the detected ridges are compared to the characteristic patterns of the bearing failure modes and the matching ridges are selected. The different stages of analysis: baselines, distance TFR, ridges detection and selection, are illustrated with actual data of damaged bearings. & 2013 Elsevier Ltd. All rights reserved.