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Fault detection and identification algorithms may rely on knowledge of underlying system dynamics while some eschew this modeling in favor of data-driven anomaly detection. This paper considers model-based residual generation and data-driven anomaly detection for a small, low-cost unmanned aerial vehicle using both types of approaches and applies those algorithms to experimental faulted and unfaulted flight-test data. The model-based fault detection strategy uses robust linear filtering methods to reject exogenous disturbances, e.g., wind, and provide robustness to model errors. The data-driven algorithm is developed to operate exclusively on raw flight-test data without detailed system knowledge. The detection performance of these complementary, but different, methods is compared.