A New Technique for Vibration-Based Diagnostics of Fatigued Structures Based on Damage Pattern Recognition via Minimization of Misclassification Probability

Vibration-based diagnostics provide various methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage recognition has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. In investigations, many techniques were applied to recognize damage in structural and mechanical systems, but recommendations on selecting the best technique for real monitoring systems are still insufficient and often contradictory. In the chapter presented, a novel technique of vibration-based diagnostics of fatigued structures based on damage pattern recognition through minimization of misclassification probability is proposed. This technique does not require the arbitrary selection of priors as in the Bayesian classifier and allows one to take into account the cases which are not adequate for Fisher’s linear discriminant analysis (FLDA). The results obtained in this chapter agree with the simulation results, which confirm the validity of the theoretical predictions of performance of the suggested technique. A numerical example is given.