Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

2020 ◽  
Vol 50 (1) ◽  
pp. 348-359 ◽  
Author(s):  
Anam Abid ◽  
Muhammad Tahir Khan ◽  
Muhammad Salman Khan
Keyword(s):  
Immune System ◽  
Fault Detection ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Bearing Fault ◽  
Bearing Fault Detection

Design of an artificial immune system based on Danger Model for fault detection

2010 ◽  
Vol 37 (7) ◽  
pp. 5145-5152 ◽  
Author(s):  
C.A. Laurentys ◽  
R.M. Palhares ◽  
W.M. Caminhas
Keyword(s):  
Immune System ◽  
Fault Detection ◽  
Artificial Immune System ◽  
Artificial Immune

ROBOT FAULT DETECTION USING AN ARTIFICIAL IMMUNE SYSTEM (AIS)

2015 ◽  
Vol 43 (2) ◽  
Author(s):  
Muhammad T. Khan ◽  
Muhammad U. Qadir ◽  
Anam Abid ◽  
Fazal Nasir ◽  
Clarence W. de Silva
Keyword(s):  
Immune System ◽  
Fault Detection ◽  
Artificial Immune System ◽  
Artificial Immune

Rolling Bearing Fault Diagnosis Based on AIS

2010 ◽  
Vol 139-141 ◽  
pp. 2569-2573
Author(s):  
Yao Bin Hu ◽  
Xia Yue ◽  
Chun Liang Zhang
Keyword(s):  
Immune System ◽  
Fault Diagnosis ◽  
Artificial Immune System ◽  
Rolling Bearing ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Clone Selection ◽  
Bearing Fault Diagnosis

In this paper, an approach of rolling bearing fault diagnosis based on artificial immune system (AIS) is presented. The features extracted from vibration signals are normalized as the original antigens, and an advanced clone selection algorithm (CSA) is applied to train the antibodies. Then use the antibody set to recognize the faults online. The experiments on rolling bearing show the approach is effective and feasible.

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