Issues for the Application of Statistical Models in Damage Detection

Many aerospace, civil, and mechanical systems continue to be used despite aging and the associated potential for damage accumulation. Therefore, the ability to monitor the structural health of these systems is becoming increasingly important. A wide variety of highly effective local nondestructive evaluation tools are available. However, damage identification based upon changes in vibration characteristics is one of the few methods that monitor changes in the structure on a global basis. The process of vibration-based damage detection will be described as a problem in statistical pattern recognition. This process is composed of four portions: 1.) Operational Evaluation, 2.)Data acquisition and cleansing; 3.) Feature selection and data compression, and 4.) Statistical model development. Current studies regarding supervised learning methods for statistical model development are discussed and emphasized with the application of this technology to a laboratory test structure. Specifically, a comparison is made between a linear discriminant classifier and a general Bayesian classifier for the purpose of determining the existence of damage.