基于小波模糊网络的电厂汽轮发电机组故障诊断

FAULT DIAGNOSIS METHOD FOR TURBOGENERATOR SET BASED ON WAVELET FUZZY NETWORK

针对传统故障诊断方法在汽轮发电机组振动类多重并发故障诊断中的局限性,提出了小波变换与模糊理论相结合的诊断方法。采用二进离散小波变换获取有效的故障特征向量,利用模糊诊断方程进行故障模式分类。通过选择足够的样本对故障诊断方程进行训练,将代表故障的信息输入训练好的诊断方程,由输出结果即可判定故障类型。实际应用表明该方法可以有效诊断汽轮发电机组振动类多重并发故障,诊断结果全面、准确。

To improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of turbogenerator sets, a new diagnosis method integrating the wavelet transform with fuzzy theory is proposed. The effective eigenvectors are acquired by binary discrete wavelet transform and the fault modes are classified by fuzzy diagnosis equation. By means of choosing enough samples to train the fault diagnosis equation and the information representing the faults is input into the trained diagnosis equation, and according to the output result the type of fault can be determined. Actual applications show that the proposed method can effectively diagnose the multi-concurrent vibrant faults of turbogenerator sets and the diagnosis result is correct.