Research for Knowledge Dynamic Acquisition of Rotating machine Fault Diagnosis

摘要：

Aimed at the condition monitoring and fault diagnosis of metallurgical fan, we have researched knowledge dynamic acquisition of rotating machine fault diagnosis and built the extensible diagnosis samples. The dynamic changing of the fault symptoms and fault mode in the diagnosis samples has been achieved. The simples databases have been treated by using the Rough Set (RS) theory, and the conflict between reduction and contradictions has been eliminated. Artificial Neural Network (ANN) was trained by the processed samples; the intelligence diagnosis can be implemented. It has high efficiency, good faulttolerant and widely adaptive capacity.

关键词： Rotating machine fault diagnosis knowledge acquisition neural network

作者: Xue-biao ZHU Kui-sheng CHEN Yong-gang DU

作者单位: College of Machine & Automation, Wuhan University of Science & Technology, Wuhan 430081,China International Economic and Trading Corporation of WISCO, Wuhan 430080, China

会议类型: 国际会议

会议名称: The Third International Conference on Modelling and Simulation(第三届国际建模、计算、仿真、优化及其应用学术会议 ICMS 2010)

会议地点: 无锡

会议语种:英文

页码: 141-144

在线出版日期: 2010-06-04（万方平台首次上网日期，不代表论文的发表时间）