基于改进HHT算法的大型回转支承故障诊断方法研究

针对大型回转支承转速较低、不稳定且实验样本稀少且故障诊断局限于人工诊断识别的问题,提出了一种基于相关函数的加权融合算法与改进HHT算法相结合的故障诊断方法:首先采用了动态适应性、抗干扰性强的基于相关函数的加权融合算法对采集的数据进行融合处理,然后以改进后的HHT作基础构造故障特征向量,再采用BP神经网络对故障类型进行特征层的识别诊断,最终确定风电回转支承的故障类型。实验结果表明,该方法有效地提高了故障诊断结果的可靠性。

Research on Fault Diagnosis Method of Large Scale Turbine Bearings Based on Improved HHT Algorithm

Aiming at the problems of low speed of large Turbine Bearings, unstable and scarce experimental samples, and fault diagnosis is limited to artificial diagnosis, a new fault diagnosis method based on correlation function of weighted fusion algorithm and improved Hilbert-Huang Transform (HHT) algorithm is proposed. Firstly, a weighted fusion algorithm based on correlation function with dynamic adaptability and anti-disturbance was adopted to deal with the collected data, then the fault feature vector was constructed based on the improved HHT. Back Propagation (BP) neural network was used to identify fault types of feature layer, and to determine the type of failure of the wind turbine at the last. The experimental results show that this method can improve the reliability of fault diagnosis effectively.