Mine ventilator fault diagnosis based on information fusion technique

摘要：

A fault diagnosis method of multi-fault-featured information fusion is proposed to improve accuracy of fault diagnosis. The multi information of this method includes stator current signal, axial vibration signal, and radial vibration signal. These collected signals are processed by wavelet analysis to extract the fault feather. Based on each type of information, primary conclusion is achieved by neural networks. In order to achieve the finally conclusion, Dempster combination rule is used to realize information fusion. The experiment result shows that the reliability of fault diagnosis with the multi-fault characteristic information fusion is improved evidently and its uncertainty decreases remarkably. It proves that the proposed method can improve the accuracy and reliability of fault diagnosis.

关键词： evidential theory information fusion fault diagnosis mine ventilator

作者: Shi Li-ping Han Li Wang Ke-wu Zhang Chuan-jua

作者单位: The School of Information and Electronic Engineering, China University of Mining and Technology, Xuz China Poly Group Corporation, Beijing 100010, China

会议类型: 国际会议

会议名称: The 6th International Conference on Mining Science & Technology ICMST 2009(第六届国际矿业科学技术大会)

会议地点: 徐州

会议语种:英文

页码: 1-5

在线出版日期: 2009-10-18（万方平台首次上网日期，不代表论文的发表时间）