A PHM system for AEW radar based on AOPS-LSSVM prognostic algorithm and expert knowledge database

In order to improve the ability of fault prognostic and the efficiency of fault diagnosis for certain AEW (airborne early warning) radar, in this paper, an APSO-LSSVM (adaptive particle swarm optimization- least squares support vector machine) fault prognostic algorithm and a fuzzy reasoning algorithm are presented, and an expert knowledge database is constructed too. Based on the APSO-LSSVM fault prognostic algorithm, fuzzy reasoning algorithm and expert knowledge database, a PHM (prognostic and health management) system is established for the AEW radar. The experiment shows that, because of using the APSO algorithm to adjust the parameters of LSSVM model, the APSO-LSSVM algorithm has a better fault prognostic ability; because of integrating the APSO-LSSVM algorithm with the fuzzy reasoning expert knowledge database, the PHM system not only can enhance the ability of health condition monitoring, but also can improve the efficiency of fault diagnosis and maintenance for the AEW radar. So, this PHM system can play a very important role in the AEW radar´s logistic support.