【源码】轴承故障诊断的集中时频分析工具：瞬态提取变换
本文发表在《IEEE仪器与测量学报》上。
This paper has appeared in the IEEE Transactions on Instrumentation and Measurement.
在工业旋转机械中，瞬态信号通常对应于一次元件的故障，如轴承或齿轮。
In industrial rotating machinery, the transient signal usually corresponds to the failure of a primary element, such as a bearing or gear.
然而，面对实际工程的复杂性和多样性，提取瞬态信号是一项极具挑战性的工作。
However, faced with the complexity and diversity of practical engineering, extracting the transient signal is a highly challenging task.
本文提出了一种新的时频分析方法，称为瞬态提取变换，可以有效地表征和提取故障信号中的瞬态分量。
In this paper, we propose a novel time-frequency analysis method termed the transient-extracting transform, which can effectively characterize and extract the transient components in the fault signals.
该方法基于短时傅立叶变换，不需要扩展参数或先验信息。
This method is based on the short-time Fourier transform and does not require extended parameters or a priori information.
采用Rényi熵、峰度等量化指标对该方法与其它经典和先进方法的性能进行了比较。
Quantized indicators, such as Rényi entropy and kurtosis, are employed to compare the performance of the proposed method with other classical and advanced methods.
比较结果表明，该方法能提供更高的能量集中时频表示，且能提取出峰度较大的瞬态分量。
The comparisons show that the proposed method can provide a much more energy-concentrated time-frequency representation, and the transient components can be extracted with a significantly larger kurtosis.
数值模拟和实验结果表明了该方法的有效性。
The numerical and experimental signals are used to show the effectiveness of our method.
本文的下载网址如下：
This paper can be found on the website,
https://ieeexplore.ieee.org/document/8676242
完整源码下载地址：
http://page5.dfpan.com/fs/4lac7j0222214269166/
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