基于集成NSET和模糊软聚类的风电机组齿轮箱状态监测

风电机组齿轮箱的故障频率和维修成本较高,有必要对其运行状态进行实时监测。非线性状态估计(NSET)算法有着对记忆矩阵依赖大、无法有效利用数据资源改善精度、实时性差等不足。为此,提出一种基于模糊软聚类和集成NSET的状态监测方法:使用模糊软聚类将历史数据分为边界有重叠的不同类别,实现工况的软划分并构造多个不同工况的NSET模型作为个体学习器;以参数回归方法作为结合器,可在不影响实时性的同时,使用大量数据训练参数以改善精度。用某2 MW风电机组的齿轮箱故障数据进行验证,结果表明,相比常规方法,提出方法的精度和实时性均更优;通过预测残差均值和基于残差构造的健康指数,能够灵敏、准确的反映齿轮箱的早期故障及其发展趋势。

Condition monitoring of wind turbine gearbox based on ensemble nonlinear state estimation technique and soft fuzzy clustering

The failure frequency and maintenance cost of wind turbine gearbox are relatively high. It is necessary to monitor its operation condition in real time. Nonlinear state estimation technique (NSET) has the problems of high dependence on memory matrix, low accuracy caused by insufficient data utilization, bad real time performance, etc. Therefore, a condition monitoring method based on soft fuzzy C means clustering (SFCM) and ensemble NSET is proposed. SFCM is adopted to divide the historical data into different classes with overlapping boundaries to achieve the soft condition division. NSET models under different conditions are constructed as individual learner. The parametric regression method is used as the combiner. Lots of data can be used to train the parameters without affecting the real time performance and the accuracy can be improved accordingly. The gearbox fault data of a 2 MW wind turbine are taken to evaluate the method. Compared with NSET, experimental results show that the proposed method has better accuracy and real time performance. Through the means of predicted residual and the health index based on residual, it can reflect the early fault and its development trend of gearbox sensitively and accurately.