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Fault Diagnosis algorithm of Wind Power Gearbox Based on Fuzzy Neural Network

机译:基于模糊神经网络的风电齿轮箱故障诊断算法

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To improve the accuracy of fault diagnosis in wind power gearbox system, a fault diagnosis method based on the fuzzy neural network is proposed in this paper. Due to multiple working conditions of gearbox, the accuracy of fault diagnosis is easily affected by working environment and vibration signal of gearbox. Moreover, the vibration signals of the gearbox have the characteristics of non-linearity, non-stationarity and complexity. In this paper, the fault features of vibration signals are extracted from the perspective of information fusion. Based on the data obtained from the signals analysis, the fuzzy network is employed to establish the fault diagnosis model, and the parameter learning algorithm of network is also provided. Simulation results show that the feature extraction method adopted can well reflect the fault features of gear box, and the fault diagnosis system established by fuzzy neural network also has relatively accurate fault recognition capability.
机译:为了提高风力发电齿轮箱系统故障诊断的准确性,提出了一种基于模糊神经网络的故障诊断方法。由于齿轮箱的工作条件多种多样,因此故障诊断的准确性容易受到齿轮箱的工作环境和振动信号的影响。此外,齿轮箱的振动信号具有非线性,非平稳性和复杂性的特征。本文从信息融合的角度提取了振动信号的故障特征。基于信号分析得到的数据,采用模糊网络建立故障诊断模型,并提供网络参数学习算法。仿真结果表明,所采用的特征提取方法能够很好地反映齿轮箱的故障特征,并且通过模糊神经网络建立的故障诊断系统也具有相对准确的故障识别能力。

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