NONLINEAR SYSTEM IDENTIFICATION FOR REAL-TIME STRUCTURAL HEALTH MONITORING IN COMPOSITE MATERIALS

FIBER/RESIN COMPOSITE FABRICATION TECHNOLOGIES NOW CAN PRODUCE HIGH-STRENGTH, LOW WEIGHT STRUCTURAL COMPONENTS, WHILE FIBER-OPTIC SENSING TECHNOLOGY PROVIDES A MEANS FOR SENSING VARIOUS STRUCTURAL PROPERTIES SUCH AS STRESS, STRAIN, AND ELASTICITY WHEN THESE SENSORS ARE MOUNTED ON, OR EMBEDDED WITHIN, A MATERIAL STRUCTURE. COMPOSITE STRUCTURES MAY DEGRADE DUE TO IMPROPER MANUFACTURE, HYSTERETIC WEAR, IMPACTS, AND CORROSION. WHILE EMBEDDED SENSORS PROVIDE A MEANS FOR GATHERING VAST QUANTITIES OF DATA RELATING TO THE STRUCTURAL AND DYNAMIC HISTORY OF COMPOSITE STRUCTURES, IT IS NECESSARY TO PROCESS, INTEGRATE, AND INTERPRET THIS INFORMATION IF THE EMBEDDED SENSORS ARE TO BE USEFUL IN DETERMINING STRUCTURAL INTEGRITY, USING METHODS THAT CAN DEAL WITH THE DISTRIBUTED AND NONLINEAR NATURE OF THE PROBLEM. THIS RESEARCH UTILIZES ADVANCED NEURAL-NETWORK BASED TECHNIQUES FOR MODELING AND ANALYZING DYNAMIC STRUCTURAL INFORMATION, AS WELL AS NEURAL NETWORK CLASSIFIERS FOR PERFORMING REAL-TIME SENSOR INTERPRETATION. THIS WILL YIELD AN INTELLIGENT SYSTEM FOR PROCESSING, INTEGRATING, AND INTERPRETING EMBEDDED SENSOR DATA TO GIVE A REAL-TIME, ROBUST, ACCURATE MEASURE OF STRUCTURAL INTEGRITY FOR THICK-SECTION COMPOSITE STRUCTURES.