An improved instantaneous frequency estimation algorithm for rotating machines based on a kernel-based fuzzy C-means clustering (KFCM) algorithm used in association with a spectral centroid algorithm is proposed in this study. The clustering algorithm is used first to discriminate the time-frequency points from the sources of the reference axis and other points. The discrete time-frequency points related to the instantaneous rotation frequency of the reference axis are then located based on the values of the time-frequency matrix elements; on the basis of these elements, the instantaneous rotation frequency is then estimated using a spectral centroid algorithm. It is demonstrated that this method effectively reduces the effects of interference and noise while achieving higher estimation precision. To validate the proposed method, numerical simulations of multi-component signals and crossover signals are performed. The results of these simulations indicate that the method can realize instantaneous frequency estimation with high precision, even when the numerical responses are contaminated by Gaussian white noise. In addition, when this method is used to analyze the vibration signal of rotating machinery in the situation of a run-up procedure, remarkable speed estimation results are obtained.

提出了一种改进的旋转电机瞬时频率估计算法，该算法基于与谱质心算法结合使用的基于核的模糊C均值聚类（KFCM）算法。首先使用聚类算法从参考轴源和其他点中区分出时频点。然后，根据时频矩阵元素的值来定位与参考轴的瞬时旋转频率相关的离散时频点；在这些元素的基础上，然后使用频谱质心算法估算瞬时旋转频率。实践证明，该方法有效降低了干扰和噪声的影响，同时实现了较高的估计精度。为了验证提出的方法，进行了多分量信号和交叉信号的数值模拟。仿真结果表明，即使数值响应受到高斯白噪声的污染，该方法也可以实现高精度的瞬时频率估计。此外，在起步过程中使用该方法分析旋转机械的振动信号时，可获得明显的速度估算结果。