文章基本信息

标题：A combined kernel-based fuzzy C-means clustering and spectral centroid for instantaneous frequency estimation
本地全文：下载
作者：Zhai Hua ; Mao Hong-min ; Wang Dong 等
期刊名称：Advances in Mechanical Engineering
印刷版ISSN：1687-8140
电子版ISSN：1687-8140
出版年度：2020
卷号：12
期号：9
页码：1-12
DOI：10.1177/1687814020913679
语种：English
出版社：Sage Publications Ltd.
摘要：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.
关键词：Instantaneous frequency estimation; kernel-based fuzzy C-means clustering; spectral centroid; time-frequency analysis; clustering algorithms; rotating machine