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In this paper, two statistical schemes aiming at effective fault detection and isolation (FDI) for aircraft systems are introduced. They are based on novel stochastic pooled nonlinear autoregressive moving average with exogenous excitation representations that model the relationships among available aircraft signals, as well as statistical decision making. The first, or ldquodirect,rdquo scheme relates a pilot input to a measurable flight attitude via a two-stage pooled representation. The second, or ldquoindirect,rdquo scheme relates four attitude-dependent flight variables via a proper pooled representation. Both schemes achieve effective FDI operation inside an entire flight regime, under stochastic effects and uncertainty and under various operating or environmental conditions, at the price of increased computational effort during training. Their performance and robustness are assessed via many flights conducted with an aircraft simulator inside the considered flight regime, under different conditions and under faults of various types and magnitudes.