In the literature of fault diagnosis for machinery, mechanical vibration signal analysis serves one of the most important approaches. By extracting features of vibration signal, such as instantaneous frequency (IF), instantaneous amplitude or spectral kurtosis, the fault of machinery can be effectively diagnosed. As an efficient tool of analyzing vibration signal, time–frequency (TF) analysis (TFA) technology has been widely employed in this area. Restricted by Heisenberg uncertainty theory, the TF resolution of the traditional linear TF analysis technique may not be optimal. To overcome this problem, in this paper, inspired by wavelet-based synchrosqueezing transform (WSST), synchroextracting transform (SET) and the ideal TFA principle, we present a novel wavelet-based TFA approach, which is named wavelet-based synchroextracting transform (WSET) and acts as a TF post-processing technology. The core idea of WSET is that we only extract wavelet transform TF spectrum of signal in scale correspond to IF, and get rid of burry TF energy. This proposed method is capable of enhancing the concentration of TF representation. In the current study, firstly, the theory of the WSET is analytically deduced. Secondly, the validity of WSET is proved through processing the nonstationary and multi-component bat signal. Finally, we employ two benchmarks of rotor and rolling bearing to verify the effectiveness of WSET to extract the failure features for malfunction diagnosis.

在机械故障诊断的文献中，机械振动信号分析是最重要的方法之一。通过提取振动信号的瞬时频率（IF）、瞬时幅度或谱峰度等特征，可以有效地诊断机械故障。作为分析振动信号的有效工具，时频（TF）分析（TFA）技术已广泛应用于该领域。受海森堡不确定性理论的限制，传统线性挠场分析技术的挠场分辨率可能不是最优的。为了克服这个问题，在本文中，受基于小波的同步压缩变换（WSST）、同步提取变换（SET）和理想 TFA 原理的启发，我们提出了一种新的基于小波的 TFA 方法，它被命名为基于小波的同步提取变换（WSET），作为一种 TF 后处理技术。WSET 的核心思想是我们只提取信号的小波变换 TF 谱对应于 IF 的尺度，并去除 burry TF 能量。这种提出的方​​法能够提高 TF 表示的浓度。在目前的研究中，首先对WSET的理论进行了解析推导。其次，通过对非平稳多分量蝙蝠信号的处理，证明了WSET的有效性。最后，我们采用转子和滚动轴承两个基准来验证 WSET 提取故障特征以进行故障诊断的有效性。并摆脱 burry TF 能量。这种提出的方​​法能够提高 TF 表示的浓度。在目前的研究中，首先对WSET的理论进行了解析推导。其次，通过对非平稳多分量蝙蝠信号的处理，证明了WSET的有效性。最后，我们采用转子和滚动轴承两个基准来验证 WSET 提取故障特征以进行故障诊断的有效性。并摆脱 burry TF 能量。这种提出的方​​法能够提高 TF 表示的浓度。在目前的研究中，首先对WSET的理论进行了解析推导。其次，通过对非平稳多分量蝙蝠信号的处理，证明了WSET的有效性。最后，我们采用转子和滚动轴承两个基准来验证 WSET 提取故障特征以进行故障诊断的有效性。