A two-stage method for bearing fault detection using graph similarity evaluation.

• Based on graph similarity evaluation, a two-stage method is proposed for bearing fault detection. • Graph similarity is verified as an effective utensil for the bearing operation monitoring. • With only few sampling points (100 samples in this research), the operation states can be rapidly identified. Robust identification of bearing health states is closely linked to timely condition monitoring and downtime reducing for rotating machinery. Although many proposed algorithms achieve extraordinary performances on feature extraction, uncertainty still remains for the bearing fault identification. To address this problem, this paper introduces a two-stage framework for bearing fault detection using graph similarity evaluation. The recognition stage is used to identify the operation state (fault or not) based on an improved graph-based method according to the sampled vibration signal for each spindle turn. The feature extraction stage, on the other hand, is implemented to extract the fault characters from the time-domain signals. The results indicate that the proposed method achieves 100% identification accuracy in bearing fault detection even with phase shifts. This work therefore provides a powerful tool for bearing faults detection and is broadly applicable to a variety of engineering applications and experimental conditions. [ABSTRACT FROM AUTHOR]