SURVEYS FOR ARTIFICIAL IMMUNE RECOGNITION SYSTEM AND COMPARISON WITH ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES IN INTELLIGENT FAULT DIAGNOSIS OF ROTATING MACHINES

In this Paper, surveys, application, and comparison of three types of artificial intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and Artificial Immune Recognition System have been introduced. Selecting the correct features is the most important thing in training and diagnosis field and it is the core issue of this field, in this thesis, a trial is made to improve the accuracies of the three proposed methods by trying to select the proper features from time domain. The training is done by using the data collected from two-channel, horizontal and vertical in three cases first, both time and frequency domains are used as features input to the three proposed methods, secondly, using frequency domain only or thirdly, using part of the time domain features with frequency domain features; for two speed. All the three methods show excellent accuracy when training and diagnosis at same specific speed especially SVM, while the accuracy is low when diagnosis at a speed that differs from training speed. Also all the three methods give excellent diagnosis results when the applied load at the same speed of training speed