Order spectrogram visualization for rolling bearing fault detection under speed variation conditions

For rotating machinery condition monitoring and fault diagnosis under speed variation conditions, order tracking has been considered as a very powerful non-stationary vibration signal analysis method, when compared with the frequency analysis methods based on stationary assumption. However, the conventional order tracking methods require additional hardware to provide a phase reference signal. This constraint limits the conventional methods in industrial applications significantly. In order to further improve the applicability of the conventional order tracking methods, some tacho-less order tracking methods have been proposed in the past few years. Despite the tacho-less order tracking methods successfully getting rid of reference signal, the algorithms are very complex and the computational burden is increased correspondingly. To address the aforementioned shortcomings, a straightforward tacho-less order tracking method based on order spectrogram visualization is proposed in this paper. In the proposed method, a ridge extraction approach is used to estimate the instantaneous frequency of a certain rotating frequency harmonic. And then the vibration signal is resonance demodulated and the time-frequency distribution of the demodulated signal is obtained. A subsequent transform is conducted and the frequency axis of the time-frequency distribution is resealed based on the estimated instantaneous frequency of rotating shaft. Then, an order spectrogram is constructed and thereby the non-stationary interference introduced by rotating speed fluctuation is suppressed. Finally, fault orders are uncovered and bearing fault type can be identified. The effectiveness of the proposed method has been validated by both simulated and experimental rolling bearing vibration signals. The results illustrate the improved features regarding previously developed tacho-less order tracking method in bearing diagnosis under speed variation conditions. (C) 2018 Elsevier Ltd. All rights reserved.