Paper Publications

DOI number:10.1155/2015/563954

Journal:Mathematical Problems in Engineering

Abstract:It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis.This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with 𝐾-nearest neighbor (KNN) algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk.

Co-author:张清华,彭志平,徐维超

First Author:熊建斌

Indexed by:Journal paper

Correspondence Author:孙国玺

Volume:2015

Page Number:1-9

Translation or Not:no

Date of Publication:2015-02-03

Included Journals:SCI