Zhang Boyao, Miao Yonghao, Lin Jing, Li Hao
School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China.
School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China; Advanced Manufacturing Center, Ningbo Institute of Technology, Beihang University, Ningbo 315100, China; Science & Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China.
ISA Trans. 2022 Apr;123:398-412. doi: 10.1016/j.isatra.2021.05.012. Epub 2021 May 19.
The key idea behind demodulation analysis for bearing diagnosis is to determine the fault-induced frequency band and directly detect the potential bearing fault characteristic frequency (FCF) in the demodulated spectrum. Till now, most demodulation methods are based on the optimal selection of only one informative frequency band. However, the unwanted in-band noise will be retained or some fault information may be ignored in the case of the discrete resonant frequency band or multiple informative frequency bands. To address the issue, a FCF-oriented criterion is proposed to determine all the informative frequency bands rather than only one specified frequency band. A new weighting vector is obtained to control the contribution of each spectral frequency in the demodulated spectrum. Subsequently, a weighted envelope spectrum (WES) is introduced by integrating the spectral correlation over the full spectral frequency band and assigning the new weighting vector on each spectral frequency. In this way, all frequency components with fault information are enhanced while other components are inhibited. Furthermore, expanded to the diagnosis of compound-fault, the FCF-oriented criterion can provide the different weighting vectors relevant to the different potential faults, and the separated fault features can be identified directly in the generated WESs. Finally, the advantages of WES over the traditional methods are testified by the simulated signal and experimental data.
用于轴承诊断的解调分析背后的关键思想是确定故障引起的频带，并在解调频谱中直接检测潜在的轴承故障特征频率（FCF）。到目前为止，大多数解调方法仅基于一个信息频带的最优选择。然而，在离散谐振频带或多个信息频带的情况下，不需要的带内噪声将被保留，或者一些故障信息可能会被忽略。为了解决这个问题，提出了一种面向FCF的准则来确定所有信息频带，而不是仅一个指定频带。获得了一个新的加权向量来控制解调频谱中每个频谱频率的贡献。随后，通过在整个频谱频带上积分频谱相关性并在每个频谱频率上分配新的加权向量，引入了加权包络谱（WES）。通过这种方式，所有带有故障信息的频率分量都得到增强，而其他分量则受到抑制。此外，扩展到复合故障诊断，面向FCF的准则可以提供与不同潜在故障相关的不同加权向量，并且可以在生成的WES中直接识别分离的故障特征。最后，通过模拟信号和实验数据验证了WES相对于传统方法的优势。
