Due to the heavy background noise and strong interference, a blurry or even false time-frequency (TF) representation (TFR) by synchrosqueezing wavelet transform (SST) always results in inaccurate or meaningless gear fault extractions. To solve this problem, a novel TF analysis (TFA) method termed dual-core denoised SST is proposed in this article. The concepts critical to the proposed method are: 1) The dual-core denoising is proposed. First, the measured signal is transformed to the binary tree structure of the prewhitening and pseudo-characterizing signals by cepstrum editing. Second, two empirical mode decomposition (EMD)-based denoising techniques are, respectively, proposed for each binary-tree signal, focused on extracting different gear features such as the modulation and transients. Then, the purified feature signal is obtained by mixing the dual-core denoising results. 2) The multistep denoising strategy is studied for further improving the denoising accuracy. The steps are determined by the Rényi entropy. 3) By the multistep dual-core denoising, the denoised TFR with such meaningful TF signatures of gear faults as the accurate demodulation components and distinct transients could be obtained for gear fault detection. The repeatable simulations and engineering applications are used to verify the performance of denoising and TF resolution enhancement for gear fault detection.

中文翻译：

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用于齿轮故障检测的双核去噪同步压缩小波变换


由于背景噪声大、干扰强，同步压缩小波变换(SST)的时频(TF)表示(TFR)往往模糊甚至错误，导致齿轮故障提取不准确或无意义。为了解决这个问题，本文提出了一种新的TF分析（TFA）方法，称为双核去噪SST。该方法的关键概念是：1）提出了双核去噪。首先，通过倒谱编辑将测量信号转换为预白化和伪表征信号的二叉树结构。其次，分别针对每个二叉树信号提出了两种基于经验模式分解（EMD）的去噪技术，重点是提取不同的齿轮特征，例如调制和瞬态。然后，通过混合双核去噪结果得到纯化的特征信号。 2）研究多步去噪策略，进一步提高去噪精度。步骤由 Rényi 熵决定。 3）通过多步双核去噪，可以获得具有齿轮故障有意义的TF特征的去噪TFR，如精确的解调分量和明显的瞬态，用于齿轮故障检测。可重复的仿真和工程应用用于验证齿轮故障检测的去噪和TF分辨率增强的性能。