Chen Bingyan, Cheng Yao, Zhang Weihua, Gu Fengshou, Mei Guiming
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China.
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.
ISA Trans. 2022 Aug;127:395-414. doi: 10.1016/j.isatra.2021.08.025. Epub 2021 Aug 24.
Identifying a spectral frequency band with abundant fault information from spectral coherence is essential for improved envelope spectrum-based bearing diagnosis. Both blind features and targeted features have been employed to distinguish informative spectral frequency band of spectral coherence. However, how to select appropriate feature to correctly discriminate the optimal frequency band of spectral coherence in different scenarios is problematic. In this study, a new targeted feature is presented to quantify the signal-to-noise ratio in narrow frequency bands of spectral coherence, and further a method based on the proposed feature is developed to distinguish an optimal spectral frequency band of spectral coherence for bearing diagnostics. The efficiency of the developed method, typical blind feature-based methods and typical targeted feature-based methods in identifying the defect-sensitive frequency band of spectral coherence and bearing fault diagnosis is validated and compared using simulated signals with different interference noises and bearing experimental signals. The advantages and limitations of typical blind and targeted feature-based methods in different scenarios are summarized to guide the application. The results demonstrate that the developed targeted feature can efficiently evaluate bearing failure information in the cyclic frequency domain, and the presented approach can accurately discriminate the failure-related spectral frequency band of spectral coherence and detect different bearing faults compared with the methods based on the state-of-the-art features.
从谱相干中识别出具有丰富故障信息的频谱频段对于改进基于包络谱的轴承故障诊断至关重要。盲特征和目标特征都已被用于区分谱相干的信息性频谱频段。然而，如何在不同场景中选择合适的特征来正确区分谱相干的最优频段是个问题。在本研究中，提出了一种新的目标特征来量化谱相干窄频段中的信噪比，进而开发了一种基于该特征的方法来区分用于轴承诊断的谱相干最优频谱频段。使用具有不同干扰噪声的模拟信号和轴承实验信号，验证并比较了所开发方法、典型的基于盲特征的方法和典型的基于目标特征的方法在识别谱相干的缺陷敏感频段和轴承故障诊断方面的效率。总结了典型的基于盲特征和目标特征的方法在不同场景中的优缺点，以指导应用。结果表明，所开发的目标特征能够有效地评估循环频域中的轴承故障信息，并且与基于现有特征的方法相比，所提出的方法能够准确地区分与故障相关的谱相干频谱频段并检测不同的轴承故障。
