An Health Condition Assessment Model Based on Immune Genetic Algorithm and Ordinal Support Vector Class Machine for DTG

Aiming at the problem of health condition assessment for dynamically tuned gyroscope under unsupervised case, a two-stage health assessment model was proposed. First, by utilizing HHT method to preprocess data to overcome problems of sensitivity to center selection of FCM and prematurity of genetic algorithm, a weighted immune genetic fuzzy C-means clustering model was introduced. Then, according to the order of the clustering result data, an assessing health condition model based on ordinal support vector class machine was proposed. The experimental results show that the clustering model has higher convergence precision and speed;and the assessment model has higher accuracy and efficiency.