基于改进AAKR的风电机组齿轮箱状态监测

Condition monitoring of wind turbine gearbox based on improved AAKR

针对自联想核回归(AAKR)算法在计算相似度时未考虑状态向量中各元素对欧氏距离贡献程度不一、模型参数常依据主观经验进行标定而导致模型精度较低的问题,提出基于旗鱼优化(SFO)的改进AAKR算法建立齿轮箱正常行为模型的非参数建模方法。首先,以全参数等间隔划分方法构建记忆矩阵;其次,在AAKR模型中引入距离权重系数并通过SFO算法对AAKR模型中的宽度系数和距离权重系数进行优化;最后基于滑动窗口和残差数据构造健康指数实现风电机组齿轮箱的状态监测。以某台2 MW风电机组实测数据为例进行验证,结果表明,相比于传统AAKR、加权AAKR和稳健状态估计模型,所提算法平均精度分别提高了1.55%、0.6%、0.76%,在故障预警时通过所构造的健康指数能够更灵敏、准确的反映齿轮箱的早期故障及其发展趋势。

The auto associative kernel regression(AAKR) algorithm does not consider the contribution of each element in the state vector to the Euclidean distance when calculating the similarity, and the model parameter is often calibrated based on subjective experience. As a result, the accuracy of the model is relatively low. A non parametric modeling method for establishing the normal behavior model of gearbox is proposed based on the SFO algorithm and the modified AAKR algorithm. Firstly, the memory matrix is constructed by full parameter equal interval partition method. Secondly, the distance weight coefficient is introduced into the AAKR model, and the width coefficient and distance weight coefficient in the AAKR model are optimized by SFO algorithm. Finally, the health index is constructed based on sliding window and residual data to realize the condition monitoring of wind turbine gearbox. Taking the measured data of a 2 MW wind turbine as an example, the results show that compared with the traditional AAKR, weighted AAKR and robust state estimation model, the average accuracy of the proposed algorithm is improved by 1.55%, 0.6% and 0.76% respectively. In fault early warning, the constructed health index can more sensitively and accurately reflect the early fault and development trend of gearbox.