1. INTRODUCTION Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines 3.2.1. Initialization The first part of the algorithm is to prepare the data for system variables and the training process. Before using the training data, it should be normalized to ensure the numeric range between (0, 1) at each attribute. Through the training process an affinity measure by using the inverted Euclidean distance. The maximum distance measured should be in range of (0, 1) between any two recognition cells or antigen and recognition cell, which can be done by adding the following step to the data normalization process: Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines Figure (2) - ARB cell refinement through competition for limited resources To control the size of the ARB pool and renew those ARBs with greater stimulation (and thus affinity) to the antigen being trained a Competition for limited resources is used. The stop condition in the middle of the loop allows the final step of clone generation to be avoided when he ARB pool reaches a desirable condition. In this process only ARBs of the same class as the antigen are considered, meaning that the class of an ARB is never adjusted in the mutation process. The final step sees each ARB in the pool have mutated clones generated using the same clonal expansion and somatic hypermutation steps used previous to generate mutated clones of he best match from the memory cell. Here the number of clones generated for each ARB in the pool is calculated as the following: marl brane — atiean YG wnilnwawn!l'D vita (14 Figure (3) MSF components Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines Figure (4) Accelerometers Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines The setting operation for IDAC-6c software interface for the three speeds are Figure (8) main software view Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines Figure (10) shows vibration analysis for two channels as specific speed by browsing Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines Misalignment is the vertically or angularly offsetting in centerline between the driving shaft and the driven machine. There are two types of misalignment depending on the type of the offsetting, parallel and angular misalignment or combined. Figure (13) shows angular misalignment if offset one side and parallel misalignment if offset two sides. Figure (13) Misalignment Surveys For Artificial Immune Recognition System and Comparison with Artificial Neural Networks and Support Vector Machines in Intelligent Fault Diagnosis of Rotating Machines Table (1) the accuracies of diagnosis after training at 12.5 Hz using all frequency and time domain features .2. Training at 25Hz with using all time and frequency domain features Table (2) the accuracies of diagnosis after training at 25 Hz with using all frequency and time domain features 5.3. Training at 12.5Hz with using frequency domain features only Table (3) the accuracies of diagnosis after training at 12.5 Hz with using all frequency domain features only 5.4. Training at 25Hz with using frequency domain features only The accuracies of training 15% validation in each method are 92.83% for ANN and 96.7% for SVM and 88.89% for AIRS and the accuracies of diagnosis after training at 25 Hz (1500 RPM) are shown in the table below: Table (4) the accuracies of diagnosis after training at 12.5 Hz with using all frequency domain features only 5.5. Training at 12.5Hz with using (RMS, o,Kur, SK and CrF) from time domain and all frequency domain features Table (5) the accuracies of diagnosis after training at 12.5 Hz with using some of time domain features and frequency domain features 5.6. Training at 25Hz with using (RMS, o,Kur, SK and CrF) from time domain and frequency domain features From the table (1) the three methods showing good accuracies when diagnosis at same speed especially SVM. AIRS shows is better than ANN and SVM in diagnosis at 12.5 Hz when using all time and frequency domains features, because of the noise at low speed that may intervene with features pattern; this effect of the weights of the ANN and normalization for AIRS, while SVM while comparing the new data with it hyperplane and small change in its characteristic will not effect on it classification. Table (6) the accuracies of diagnosis after training at 25 Hz with using some of time domain features and frequency domain features
