This article presents a potential analytic redundancy approach to detect faults in the air data sensor of an aircraft. In modern aircraft, fault detection of air data sensors is performed using a complex voting mechanism, which requires the availability of redundant air data sensor in all situations. However, to continuously monitor operation and performance of these sensors, the analytic redundancy-based air data estimation and fault detection is highly preferred than estimation with air data probe measurements. The proposed algorithm uses the kinematics of aircraft to estimate air data and detect air data sensor fault. In this paper, a simple mathematical model is developed, which does not consider the forces and moments acting on aircraft and uses measurements only from the Inertial Measurement Unit (IMU) and Navigation System Data (NSD). In order to implement this approach, the Iterated Optimal Extended Kalman Filter (IOEKF) is developed to estimate air data, which provides an accurate and stable estimation. With the estimated states, the physical air data sensor measurements are compared and the residual is calculated to track each sensor performance and to detect the occurrence of a fault. The key advantage of this approach is that it does not require complex dynamic equations and is free from system uncertainties. The proposed algorithm is simulated in MATLAB software using flight simulator flight data and validated using the real-time flight data of Cessna Citation II transport aircraft.

X-ray powder diffraction data, unit-cell parameters, and space group for S-2-(methylamino)-2-phenylcyclohexan-1-one hydrochloride, C13H18ClNO, are reported [a = 6.578 (1) Å, b = 13.250 (2) Å, c = 15.096 (2) Å, β = 91.619 (3)°, unit-cell volume V = 1315(1) Å3, Z = 4, and space group P21]. All measured lines were indexed and are consistent with the P21 space group. No detectable impurities were observed.