Aiming at the problem that the effectiveness of impulse feature enhancement (IFE) depends on the duration of high-level (or low-level) K and the number of high-level L, we regard a segmented impulse norm as the fitness function and combine it with the whale optimization algorithm to select the optimal parameters adaptively. Time-frequency joint noise reduction (TFJNR) is also proposed to suppress the noise components in the signal. Simulation and experimental results of rolling bearings indicate that the proposed algorithm can rapidly select the optimal parameters K and L to ensure the performance of IFE, while TFJNR has the ability to suppress the noise components in the signal. Fast kurtogram, empirical mode decomposition, and fast spectral correlation are also used for comparison. The results highlight the performance of the proposed algorithm.

针对脉冲特征增强（IFE）的有效性取决于高阶（或低阶）K的持续时间和高阶L的数量的问题，我们将分段脉冲范数作为适应度函数，并将其组合它与鲸鱼优化算法一起自适应地选择最佳参数。还提出了时频联合降噪（TFJNR）来抑制信号中的噪声成分。滚动轴承的仿真和实验结果表明，该算法可以快速选择最优参数K和L。为确保IFE的性能，而TFJNR具有抑制信号中的噪声成分的能力。快速峰图，经验模态分解和快速光谱相关性也用于比较。结果突出了所提出算法的性能。