Clonal optimization-based negative selection algorithm with applications in motor fault detection |
| |
Authors: | X Z Gao S J Ovaska X Wang M-Y Chow |
| |
Affiliation: | (1) Department of Electrical Engineering, Helsinki University of Technology, Otakaari 5 A, 02150 Espoo, Finland;(2) Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA |
| |
Abstract: | The Negative Selection Algorithm (NSA) and clonal selection method are two typical kinds of artificial immune systems. In
this paper, we first introduce their underlying inspirations and working principles. It is well known that the regular NSA
detectors are not guaranteed to always occupy the maximal coverage of the nonself space. Therefore, we next employ the clonal
optimization method to optimize these detectors so that the best anomaly detection performance can be achieved. A new motor
fault detection scheme using the proposed NSA is also presented and discussed. We demonstrate the efficiency of our approach
with an interesting example of motor bearings fault detection, in which the detection rates of three bearings faults are significantly
improved. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|