Transient-extracting transform

长期寻横/纵向项目合作。业务范围包括但不限于，数据分析、信号处理、时频分析、小波变换、故障诊断、机械结构优化，模态辨识，减振降噪，图像处理，语音识别，人工智能等。（无稳定的经费支持，原有的研究亦难以维系，不得不停下纵向研究，先拉拉赞助）邮箱：[e-mail]

This paper has appeared in the IEEE Transactions on Instrumentation and Measurement.

In industrial rotating machinery, the transient signal usually corresponds to the failure of a primary element, such as a bearing or gear. However, faced with the complexity and diversity of practical engineering, extracting the transient signal is a highly challenging task. 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. This method is based on the short-time Fourier transform and does not require extended parameters or a priori information. Quantized indicators, such as Rényi entropy and kurtosis, are employed to compare the performance of the proposed method with other classical and advanced methods. 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. The numerical and experimental signals are used to show the effectiveness of our method.

This paper can be found on the website,

https://ieeexplore.ieee.org/document/8676242