基于双谱熵和聚类分析的转子系统故障诊断

Rotor System Fault Diagnosis Based on Bispectrum Entropy and Clustering Analysis

转子系统在故障状态下的振动信号往往呈现很强的非线性,其在频域上主要表现为不同频率之间相互耦合,产生合频、差频等组合频率。为了解决传统频谱分析只关注信号中的频率成分及其幅值大小,而忽略信号相位信息的问题,采用双谱方法对振动信号进行分析。双谱包含信号相位信息并且对非线性敏感,可以将早期故障的微弱非线性放大,检测出频谱中不同频率之间的非线性相位耦合关系。通过对ZT-3转子实验台植入不同类型的故障,采集系统在不同状态下的加速度信号,从振动信号的双谱中提取各频段的信息熵,采用模糊聚类方法进行故障识别。结果表明,双谱熵作为特征参量可以准确识别转子系统的故障类型,验证了方法的可行性。

Vibration signal of rotor system in fault state tends to be highly non-linear, which is mainly manifested in the frequency domain as coupling between different frequencies, generating combined frequency, differential frequency and other combined frequencies. In order to solve the problem that traditional spectrum analysis only focuses on the frequency component and its magnitude of the signal, but ignores the phase information of the signal. Bispectrum contains signal phase information and is sensitive to nonlinearity. It can amplify weak nonlinearity of early faults and detect nonlinear phase coupling between different frequencies in the spectrum. By implanting different types of faults into ZT-3 rotor test-bed, the acceleration signals of the system in different states are collected, the information entropy of each frequency band is extracted from the bispectrum of vibration signals, and the fuzzy clustering method is used for fault identification. The result shows that the bispectrum entropy as a characteristic parameter can accurately identify the fault type of the rotor system, which verifies the feasibility of the method.