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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 563954, 9 pages
Research Article

A Diagnosis Method for Rotation Machinery Faults Based on Dimensionless Indexes Combined with -Nearest Neighbor Algorithm

1Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, China
2School of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming 525000, China
3School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Received 30 October 2014; Revised 3 February 2015; Accepted 3 February 2015

Academic Editor: Gang Li

Copyright © 2015 Jianbin Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 -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.