Title :
A Negative Selection Algorithm-based motor fault detection scheme
Author :
Gao, X.Z. ; Wang, Xiongfei ; Ovaska, S.J. ; Arkkio, Antero ; Zenger, Kai ; Xiaofeng Wang
Author_Institution :
Inf. Eng. Coll., Shanghai Maritime Univ., Shanghai, China
Abstract :
In this paper, we propose a Negative Selection Algorithm (NSA)-based motor fault detection system. Only the feature signals of the healthy motors are needed here for generating the NSA detectors. Different from the conventional fault detection approaches, no prior knowledge of the motor fault types is assumed to be known beforehand in the proposed scheme. Our motor fault detection method is examined using both the rotor and stator faults in computer simulations. We further explore its applicability in case of fault detection with varying motor loads.
Keywords :
artificial immune systems; electric machines; fault diagnosis; rotors; signal processing; stators; NSA detector; artificial immune system; electrical machine; healthy motor; motor fault detection; motor fault type; motor load; negative selection algorithm; rotor fault; signal; stator fault; Detectors; Educational institutions; Fault detection; Feature extraction; Immune system; Rotors; Stators; Artificial Immune Systems (AIS); Negative Selection Algorithm (NSA); electrical machines; fault detection; motors;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022383