Kurtogram manifold learning and its application to rolling bearing weak signal detection
Kurtogram manifold learning and its application to rolling bearing weak signal detection
复制标题
峰图流形学习及其在滚动轴承微弱信号检测中的应用
DOI：
10.1016/j.measurement.2018.06.026
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发表时间：
2018
期刊：
影响因子：
5.6
通讯作者：
Huang Tao
中科院分区：
文献类型：
--
作者：
Wang Yi;Tse Peter W.;Tang Baoping;Qin Yi;Deng Lei;Huang Tao
提出了一种用于微弱信号增强的峭图方法，并得到了验证。该方法的本质是通过最优带通滤波去噪，而对带内噪声不作处理。因此，故障诱发的瞬态脉冲在较宽的频带内传播，仍然会受到噪声的污染。针对峰图方法存在的缺陷，提出了一种新的全时频空间信号去噪的峰图流形学习方法。该方法将被峰度图分割的子信号进行融合，构建高维瞬态脉冲特征空间，通过流形学习挖掘瞬态脉冲特征空间。在此基础上，可以滤除带内噪声，从而揭示瞬态脉冲特征。实验验证结果表明，该方法优于峭图法，对滚动轴承微弱信号检测是有效的。
The kurtogram method has been proposed for weak signal enhancement and been validated very powerful. The essence of the kurtogram method is de-noising by optimal band-pass filtering, however, the in-band noise are left unprocessed. As a result, the fault induced transient impulses, which spread within a wide frequency band, would be still contaminated by noise. Aiming at the flaws encountered by the kurtogram method, this paper proposes a novel kurtogram manifold learning method for signal de-noising in whole time-frequency space. In this method, the sub-signals split by kurtogram are fused to build a high dimensional transient impulse feature space, manifold learning is conducted to mine the transient impulse feature space. On this basis, the in-band noise can be filtered out, thereby the transient impulse features will be uncovered. The experimental validation results exhibit the proposed method outperforms kurtogram method and is effective for rolling bearing weak signal detection.