摘要
故障诊断中的误报和漏报现象直接影响诊断的准确率 ,同时在线故障诊断又要求很强的实时性 .本文在给出粗糙集神经网络系统原理框图的基础上 ,结合领域知识把该系统应用于滚动轴承的故障诊断中 ,仿真实验结果表明该系统提高了故障诊断的准确率和诊断速度 ,同时减少了检测项目 ,降低了诊断成本 ,在实际中有良好的应用前景 .
The phenomena of misinformation and failing to report in fault diagnosis affect directly the quality of diagnosis, meanwhile, fault diagnosis on line demands real time. On the basis of giving an architecture of rough set neural network system, this paper applies it to the fault diagnosis of rolling bearings combined with professional knowledge. Simulation results indicate that the system has increased the quality and rate of diagnosis, reduced measure items and costs of diagnosis. There will be well application prospect in practice.\;
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2001年第5期681-685,共5页
Control Theory & Applications
基金
西安交通大学机械制造系统工程国家重点实验室开放基金资助项目
