基于优化VMD复合多尺度散布熵及LSTM的风力发电机齿轮箱故障诊断方法研究

以风力发电机齿轮箱加速度信号为研究对象,提出一种数据驱动的风力发电机齿轮箱故障诊断方法,该方法以灰狼优化的变分模态分解方法(AGWO-VMD)、复合多尺度规范化散布熵(NCMDE)及长短期记忆网络(LSTM)为基础,实现齿轮箱故障的快速诊断.首先将时域信号转换至角域;然后通过AGWO-VMD方法对角域信号进行自适应分解...

FAULT DIAGNOSIS METHOD OF WIND TURBINE’S GEARBOX BASED ON COMPOSITE MULTISCALE DISPERSION ENTROPY OF OPTIMISED VMD AND LSTM

A data driven diagnosis method based on acceleration signals for the gearbox in wind turbine is proposed, which on the basis of the grey wolves optimised variational modal decomposition （AGWO-VMD）, normalized composite multiscale dispersion entropy （NCMDE） and long short-term memeory （LSTM）, the gearbox faults diagnosis is realized rapidly. Firstly, the discrete signal in time domain is converted to angular domain. Secondly, AGWO-VMD algorithm is used to decompose the signal adaptively, and NCMDE algorithm is used to extract fault features as feature vectors from both original and decomposed signals. At last, the LSTM model is used for intelligentive classification of feature vectors. The proposed method is validated by 100 groups of data under 6 types of faults collected from WTDS, and the result shows that , it can recognize the right type of gearbox's fault rapidly and effectively.