Title :
Fault diagnosis research of rotating machinery based on Dendritic Cell Algorithm
Author :
Delong Cui;Qinghua Zhang;Jianbin Xiong;Qinxue Li;Mei Liu
Author_Institution :
College of Computer and Electronic Information
Abstract :
Rotating machinery play an important role in modern industry. Ensuring security and reliability of manufacture equipments has been receiving more and more recognition. This paper proposes an innovative techniques for rotating machinery fault diagnosis. The fault diagnosis system is comprised of clearly defined separate submodels including antigen submodel, memory submodel, DC submodel, analyse submodel and diagnositic submodel etc. Based on the system model, a novel rotating machinery fault diagnosis scheme based on Dendritic Cell Algorithm (DCA) and dimensionless parameter is proposed in this paper. To demonstrate our method, we apply our method to the real test bed of concurrent fault diagnosis for rotating machinery. Experimental result demonstrates that the method can realize effectively real-time fault diagnose for rotating machinery and has high potential applications in real project.
Keywords :
"Fault diagnosis","Machinery","Immune system","Vibrations","Indexes","Algorithm design and analysis","Frequency measurement"
Conference_Titel :
Information and Automation, 2015 IEEE International Conference on
DOI :
10.1109/ICInfA.2015.7279436