Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis
Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis
复制标题
自适应最大二阶循环平稳盲反褶积及其在机车轴承故障诊断中的应用
DOI：
10.1016/j.ymssp.2021.107736
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发表时间：
2021-03-09
影响因子：
8.4
通讯作者：
Yi, Yinggang
中科院分区：
文献类型：
--
作者：
Zhang, Boyao;Miao, Yonghao;Yi, Yinggang
最大二阶循环平稳性盲反褶积（CYCBD）在获取与轴承早期故障相关的弱周期脉冲方面优于其他反褶积方法。然而，CYCBD在实际应用中面临的主要挑战是如何合理设置几个关键参数，其中最重要的是目标循环频率或故障周期。这可能是由于CYCBD的优势被提供的时间大大削弱了。为了克服上述局限性，本文提出了一种自适应CYCBD （ACYCBD）。在该方法中，包络谐波积谱（EHPS）是一种强大的工具，用于精确估计真实循环频率或周期。然后，将估计结果而不是粗略提供的值作为目标循环频率。此外，在存在重能量谐波或强外部噪声的情况下，EHPS在故障周期识别中仍然具有较强的鲁棒性。与原始的CYCBD相比，ACYCBD可以在没有任何周期先验信息的情况下提取淹没在原始振动信号中的弱脉冲。最后，通过对某机车轴承试验台的合成信号和实验数据的处理，说明了该算法的有效性和优越性。(c) 2021 Elsevier Ltd.版权所有。
Maximum second-order cyclostationarity blind deconvolution (CYCBD) outperforms other deconvolution methods in retrieving the weak periodic impulses related to bearing incipient faults. However, the main challenge in the practical application of CYCBD is how to set several key parameters appropriately, the uppermost of which is the targeted cyclic frequency or fault period. It may attribute to the fact that the advantage of CYCBD is greatly compromised by the provided period. To overcome the above limitations, an adaptive CYCBD (ACYCBD) is presented in this article. In the proposed method, a powerful tool, envelope harmonic product spectrum (EHPS), is tailored to estimate the true cyclic frequency or period precisely. Then, the estimated result instead of a coarsely provided value is regarded as the targeted cyclic frequency. Furthermore, in the presence of heavy energy harmonics or strong external noise, EHPS still has strong robustness in the fault period identification. Compared with the original CYCBD, ACYCBD can extract the weak impulses submerged in the raw vibration signal without any prior information about the period. Finally, the effectiveness and advantages of ACYCBD are revealed by employing it on the synthesized signals and experimental data collected from a locomotive bearing test rig.(c) 2021 Elsevier Ltd. All rights reserved.