ITT变换在风电机组滚动轴承故障诊断中的应用

Application of ITT transform in fault diagnosis of wind turbine rolling bearing

为实现风电机组滚动轴承微弱故障诊断,提出了基于改进的时时(ITT)变换的风电机组滚动轴承故障诊断方法。由时时(TT)变换可得到一维轴承故障振动信号的TT变换矩阵,实现滚动轴承振动信号的二维TT表示。提取该TT变换矩阵的对角线元素可滤除低频干扰信号,起到增强故障特征的效果。鉴于噪声对TT变换分析效果具有重要影响,提出基于能量熵准则的奇异值分解降噪方法改进TT变换,以提高TT变换的抗噪能力,实现强背景噪声条件下轴承微弱故障特征提取。仿真、实验及工程应用实例结果均表明所提方法可以有效诊断出风电机组滚动轴承的故障类型。

A method based on ITT（Improved Time-Time） transform for diagnosing the faint fault of wind turbine rolling bearing is proposed. The TT transform matrix of 1-dimensional vibration signals of rolling bearing is obtained via TT transform to provide its 2-dimensional TT domain reflection. The diagonal elements of TT transform matrix are extracted to filter the low-frequency interference signals and strengthen the fault feature.Since noise has an important influence on the results of TT transform analysis,the SVD（Singular Value Decomposition） method based on the energy entropy norm is applied to enhance the anti-noise ability of TT transform and realize the faint bearing fault feature extraction in the strong noisy background. The simulative results,experimental results and engineering application demonstrate that,the proposed method can effectively diagnose the fault types of wind turbine rolling bearing.