摘要
为了能够从多方面反映水轮发电机组系统状态,实现对水轮发电机组故障模式的自动识别与准确诊断,将信息融合技术应用于水轮发电机组故障诊断系统。根据故障特征量将故障进行分类处理,采用多个并联的BP子神经网络进行水轮发电机组故障的局部诊断,获得彼此独立的证据,再运用D-S证据理论融合算法对各证据进行融合,最终实现对水轮发电机组故障的准确诊断。诊断测试实验证明:采用该诊断系统可有效地提高诊断可信度,减少诊断的不确定性。
Hydroelectric generating sets（HGS） information fusion diagnosis system was built for reflecting the HGS system state in multi-aspects, realizing automatical identification of HGS fault patterns and accurately diagnosing the faults. Aider fault feaaLre data were classified and processed, several shunt-wound BP networks were used to carry on local HGS fault diagnosis and acquire independent evidences each other. Then D-S evidence theory fusion algorithms were used to fuse evidences. Accurate HGS fault diagnosis was fulfilled finally. The diagnostic tests prove that the system is good to improve the reliability of the diagnosis and decrease the uncertainty markedly.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第2期333-338,共6页
Journal of Central South University:Science and Technology
关键词
水轮发电机
故障诊断
信息融合
证据理论
神经网络
hydroelectric generating sets
fault diagnosis
information fusion
evidence theory
neural network
