摘要
旋转机械结构复杂,不同方面的特征信号只从不同侧面反映设备的故障,都有一定的局限性。在对旋转机械进行故障诊断时,需要对设备的多种特征信号进行融合处理和综合诊断。运用多神经网络与证据理论融合的设备故障综合诊断方法,有效提高诊断结果的准确性和可靠性。
Due to complexity of the structure of rotating machine, phenomenon of rotating machine, and any kinds of signature has its localization. Thus the synthetic disposal and cooperative analysis for multi - characteristic signal of rotating machine are needed. A synthetic diagnosis method used multi - neural network and evidence theory for rotating machine fault diagnosis is adopted, The diagnostic results show accuracy and reliability are improved effectively.
出处
《煤矿机械》
北大核心
2007年第5期179-180,共2页
Coal Mine Machinery
基金
国家自然科学基金资助项目(50675209)
关键词
旋转机械
神经网络
D—S证据理论
综合诊断
rotating machine
neural networks
D- S evidential reasoning
integrated diagnosis
