Lv Yong, Pan Bingqi, Yi Cancan, Ma Yubo
The Key Laboratory of Metallurgical Equipment and Control of Education Ministry, Wuhan University of Science and Technology, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2019 Jul 17;19(14):3154. doi: 10.3390/s19143154.
The existing time-frequency analysis (TFA) methods mainly highlight the time-frequency ridges of the interested components by optimizing the time-frequency plane to facilitate the extraction of the relevant components. Generalized demodulation (GD), order tracking (OT), and other methods are generally used in conjunction with the TFA methods to realize the transition from a time-varying signal to a stationary signal, and finally identify the fault feature through a time-frequency plane. Generally, it is necessary to clarify the accuracy of the estimated components such as the rotational frequency or the fault characteristic frequency (FCF) during the operation of the GD or OT methods. Unfortunately, it is not only difficult to extract and locate rotational frequency or FCF, but also complicated in the whole estimation process. In this paper, a simple yet readable method is proposed to reveal the fault feature of time-varying signals. First, the method only needs to extract an arbitrary instantaneous frequency (IF). This is different from the GD method which needs to estimate and locate all phase functions. Then, it converts all variable frequency curves into corresponding lines parallel to the frequency axis based on the extracted IF to determine the proportional relationship between the components. Finally, to further improve the readability of the final results, we reduce the dimension of the transformed time-frequency representation to generate a two-dimensional (2D) energy-frequency map with high resolution and the same proportion. Subsequently, the performance is validated by simulated and experimental data.
现有的时频分析（TFA）方法主要通过优化时频平面来突出感兴趣分量的时频脊，以利于相关分量的提取。广义解调（GD）、阶次跟踪（OT）等方法通常与TFA方法结合使用，以实现从时变信号到平稳信号的转换，最终通过时频平面识别故障特征。一般来说，在GD或OT方法的操作过程中，有必要明确估计分量（如旋转频率或故障特征频率（FCF））的准确性。不幸的是，不仅难以提取和定位旋转频率或FCF，而且整个估计过程也很复杂。本文提出了一种简单易懂的方法来揭示时变信号的故障特征。首先，该方法只需要提取任意瞬时频率（IF）。这与需要估计和定位所有相位函数的GD方法不同。然后，基于提取的IF将所有变频曲线转换为与频率轴平行的相应直线，以确定各分量之间的比例关系。最后，为了进一步提高最终结果的可读性，我们降低了变换后的时频表示的维度，以生成具有高分辨率和相同比例的二维（2D）能量-频率图。随后，通过仿真和实验数据验证了该方法的性能。
