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.

轴承健康状态的可靠识别与旋转机械的及时状态监测和减少停机时间密切相关。尽管许多提出的算法在特征提取方面都取得了非凡的性能，但轴承故障识别仍然存在不确定性。为了解决这个问题，本文介绍了一个使用图相似度评估的轴承故障检测的两阶段框架。识别阶段用于基于改进的基于图的方法根据每个主轴转动的采样振动信号来识别操作状态（有无故障）。另一方面，特征提取阶段用于从时域信号中提取故障字符。结果表明，即使相移，该方法在轴承故障检测中也能达到100％的识别精度。因此，这项工作为轴承故障检测提供了强大的工具，并广泛适用于各种工程应用和实验条件。