Application of an information fusion method to compound fault diagnosis of rotating machinery
- Resource Type
- Conference
- Authors
- Hu, Qin; Qin, Aisong; Zhang, Qinghua; Sun, Guoxi; Shao, Longqiu
- Source
- The 27th Chinese Control and Decision Conference (2015 CCDC) Control and Decision Conference (CCDC), 2015 27th Chinese. :3859-3864 May, 2015
- Subject
- General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Feature extraction
Shafts
Accuracy
Gears
Fault diagnosis
Compounds
Genetic programming
Negative selection algorithm
Weighted evidence theory
Fusion decision
- Language
- ISSN
- 1948-9439
1948-9447
Aiming at how to use the multiple fault features information synthetically to improve accuracy of compound fault diagnosis, an information fusion method based on the weighted evidence theory was proposed to effectively diagnose compound faults of rotating machinery. Firstly multiple fault features were extracted by the genetic programming. Each fault feature was separately used to act as evidence and the initial diagnosis accuracy was regarded as the weight coefficient of the evidence. Then through the negative selection algorithm, the diagnosis ability of the local diagnosis was advanced and an impersonal means of obtaining basic probability assignment was given. Finally the fusion result was obtained by utilizing the weighted evidence theory into the decision-making information fusion for the preliminary result. By comparing the diagnosis results with other artificial intelligence algorithm, experiment result indicates that using multiple weighted evidences fusion can improve the diagnostic accuracy of compound fault.