中文摘要：

风力发电机齿轮箱故障信号为非平稳瞬态微弱信号,容易被齿轮啮合信号及其他噪声淹没.提出一种融合连续小波变换（Continuous wavelet transform,CWT）和平稳子空间分析（Stationary subspace analysis,SSA）的信号分解方法并应用于风力发电机齿轮箱故障诊断中.平稳子空间分析作为一种盲源分离技术可将高维数据分解成平稳源部分和非平稳源部分,对待分析信号各分量间的独立性没有要求且不需要任何先验信息.连续小波变换则可利用其所具有的多尺度分析特性把一维时间序列转换为不同尺度下的多维时间序列.对观测得到的一维时间序列数据进行连续小波变换得到多维时间序列作为平稳子空间分析的输入,利用平稳子空间分析方法将该多维时间序列分解为平稳源信号分量和非平稳源信号分量,对非平稳源信号进行包络谱分析得到齿轮箱故障的特征频率.该小波域平稳子空间分析方法被应用于一个实际风力发电机齿轮箱振动信号的分析,试验结果表明该方法可有效地诊断出齿轮箱中的轴承故障.

英文摘要：

Fault-related signals of wind turbine gearbox are non-stationary,transient and weak,which are often mixed together with gear meshing signals and submerged in background noise.A new wind turbine gearbox fault diagnosis method based on continuous wavelet transform（CWT） and stationary subspace analysis（SSA） is presented.The SSA is a blind source separation technique that can extract stationary and non-stationary source components from multi-dimensional signals without the need for independency and prior information of the source signals.Multi-scale analysis ability inherent in CWT allows for decomposing one dimensional signal into multi-dimensional signals,which can be naturally used as inputs to SSA to obtain the stationary parts and non-stationary parts of the original signal.Subsequently,the selected non-stationary component is analyzed by the envelope spectrum to identify potential fault-related characteristic frequency.Experimental studies from a real wind turbine gearbox test have verified the effectiveness of the presented method.