双矢时域齿轮早期微弱故障特征增强及应用

信噪比低和源信息的缺失是造成早期微弱故障难以准确判定的主要因素,针对以此问题,提出一种双矢时域变换(dual vector time-time domain transform,简称DVTD)的方法,用于完备和凸显齿轮早期微弱故障特征。方法借用全矢原理实现相互垂直的双通道振动信号的融合,保证双矢信号源信息的完整。在此基础上,结合双时域变换理论,提取二维时间序列的主对角元素用以构建完整的、故障特征增强的时域振动信号。以风电机组齿轮箱为实验对象,提取表征信号波动强度的小尺度指数作为状态特征,验证了双矢时域变换的微弱故障特征增强特性及其在齿轮早期微弱故障识别中应用的有效性。

Gear Early Weak Fault Feature Enhancement in Dual Vector Time-Time Domain

Low signal-to-noise ratio (SNR) and lack of source information are the main factors leading to early fault inaccurate detection. In order to solve this problem, a dual vector time-time domain (DVTD)method is proposed to complete and highlight the early weak fault characteristics of gears. In this method, the full vector principle is used to realize the fusion of the vertical double channel vibration signals, so as to obtain dual vector signal having more complete source information. On this basis, combined with the time-time domain transform theory, the principal diagonal elements of two-dimensional time series are extracted to construct a complete and fault feature enhanced time-domain vibration signal. Finally, the wind turbine gearbox is taken as the experimental object, and the index at small scale characterizing the fluctuation strength of the signal is extracted as the state feature. The classification results verify the validity of the dual vector time-time domain weak fault feature enhancement method in gear early fault identification.