The primary objective of damage detection is to ascertain with confidence if damage is present or not within a structure of interest. In this study, a damage classification problem is cast in the context of the statistical pattern recognition paradigm. First, a time prediction model, called an Auto Regressive-Auto Regressive model with Exogenous inputs (AR-ARX), model is fit to a vibration signal measured during a normal operating condition of the structure. When a new time signal is recorded from an unknown state of the system, the prediction errors are computed for the new data set using the time prediction model. When the structure undergoes structural degradation, it is expected that the prediction errors will increase for the damage case. LA-UR-02-998: will be submitted for publication of International Journal of Structural Health Monitoring 2Based on this premise, a damage classifier is constructed using a sequential hypothesis testing technique called a sequential probability ratio test (SPRT). The SPRT is one form of parametric statistical inference tests and the adoption of the SPRT to damage detection problems can improve the early identification of conditions that could lead to performance degradation and safety concerns. The sequential test assumes the probability distribution of the sample data sets, and a Gaussian distribution of the sample data sets is often assumed. This assumption, however, might impose potentially misleading behavior on the extreme values of the data i.e. those points in the tails of the distribution. As the problem of damage detection specifically focuses attention on the tails, the assumption of normality is likely to lead the analysis astray. To overcome this difficulty, the performance of the sequential hypothesis test is improved by integrating extreme values statistics, which specifically model behavior in the tails of the distribution of interest, into the sequential probability ratio test.

An experimental study was undertaken to assess the aerodynamic benefits of flapping in forward flight at various Reynolds numbers and flapping frequencies. The test article is a commercially available ornithopter whose flapping wing is a flexible membrane. Analysis has focused on the time-averaged aerodynamic performance (i.e. mean lift and thrust/drag). Results to date reveal a consistent trend in the variation of the thrust produced with changes in flapping frequency and angle of attack. These results are presented in terms of classical aircraft performance parameters, e.g. cycle-averaged lift and drag coefficients. The average effect of the flexible flapping wing on vehicle performance is thus expressed in familiar terms, parametrized by the flapping frequency.

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An effective integrated structural health monitoring system must include a method of sensing and a process of damage identification that are optimized to work together. The result is a system that provides an automated and quantified assessment of damage present in a structure. Two candidates for such a symbiosis of sensing and damage identification are impedance-based measurement and statistical process control. The impedance-based structural health monitoring method uses a high frequency signal to excite a structure through a bonded piezoelectric patch and measures the impedance response of the excited structure across a frequency spectrum. In structural damage cases such as threads loosening or a crack developing, the structure in question will begin to show a change in impedance. Once measured, a damage sensitive feature from this impedance change can be statistically quantified into different damage cases by statistical process control. This paper addresses impedance measurements from experimental structures and a subsequent statistical method for quantitatively determining when the impedance signature of the structures has changed significantly enough to warrant the classification of "damaged". Simple features and hypothesis testing algorithms are explored in an effort to create real-time solutions and reduce the complexity of damage identification for future use in low resource integrated structural health monitoring systems.