Abstract
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.
Highlights
Vibration signal analysis is widely used in traditional condition monitoring and fault diagnosis applications
With the aim of compensating for the errors introduced by noise during estimation processes, we propose a novel rotation speed estimation approach composed of a kernel-based clustering algorithm with fuzzy Cmeans and a frequency spectrum centroid searching algorithm
The following explains how the proposed method based on the kernel-based fuzzy Cmeans clustering (KFCM) algorithm and the spectrum centroid algorithm is used to extract the instantaneous frequency given by the TF matrix from the short time Fourier transform (STFT)
Summary
Vibration signal analysis is widely used in traditional condition monitoring and fault diagnosis applications. Keywords Instantaneous frequency estimation, kernel-based fuzzy C-means clustering, spectral centroid, time-frequency analysis, clustering algorithms, rotating machine With the aim of compensating for the errors introduced by noise during estimation processes, we propose a novel rotation speed estimation approach composed of a kernel-based clustering algorithm with fuzzy Cmeans and a frequency spectrum centroid searching algorithm.
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