基于多传感器信息融合和神经网络的汽轮机故障诊断研究

Research on fault diagnosis of turbine based on multi-sensor information fusion and neural network

针对传统故障诊断方法存在的诊断准确性不高的问题,提出了基于D-S证据理论的多传感器信息融合技术与BP神经网络相结合的方法,实现对汽轮机的机械故障诊断。由多个传感器采集振动信号,分别经小波变换特征提取后获得故障特征值,再经BP神经网络进行故障局部诊断,得到相应传感器对故障类型的基本可信任分配函数值,即获得彼此独立的多个证据,然后运用D-S证据理论对各证据进行融合,最终完成对汽轮机机械故障的准确诊断。实验结果表明,该方法克服了单个传感器的局限性和不确定性,是一种有效的故障诊断方法。

For the reasons of low fault diagnosis accuracy of traditional diagnosis methods, a fault diagnosis method fusing BP neural network and muhi-sensor information fusion technique based on D-S evidence theory was presented to realize machinery fault diagnosis of turbine. The fault features of the vibration signals multi sensors sample were extracted by using wavelet transform, and after these fault features were locally diagnosed through BP neural network the basic reliability distribution values of corresponding fault were got, namely multi independent evidences were got. Then all the evidences were fused using D-S evidence theory and veracious machinery fault diagnosis of turbine was realized. Experiment result shows that the presented method of fauh diagnosis overcomes the limitation and uncertainty of single sensor and it is a valid method.