Fault detection and diagnosis in a spacecraft attitude determination system

Abstract

This paper presents a new scheme for fault detection and diagnosis (FDD) in spacecraft attitude determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the FDD scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. Fault isolation is performed through extended Kalman filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the fault. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable fault signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations even if only one type of sensors is functioning properly.

Constrained low-cost GPS/INS filter with encoder bias estimation for ground vehicles׳ applications

“…At, t ¼ t N , after N measurement residuals and statistics are stored, Eqs. (17) and (21) are used to find the sample mean and sample covariance. For t 4 t N , the sample mean and sample covariance can be calculated recursively as …”

“…Estimating attitude and gyroscope biases was studied in [17]. In this study, data from sensors was fused using a linearized KF.…”

Satellite Fault Detection and Isolation Scheme with Modified Adaptive Fading EKF

“…The FDI method is proposed in the navigation and control system of deep space satellites. In addition, the method uses the Shiryayev sequential probability ratio test to estimate the probability of the fault signal given the residuals generated from either parity relationships, fault detection filters or GLR test [17,18]. Patton and Uppal [8,19] presented a robust approach to FDI residual generator that is described to realize a diagnostic scheme for the satellite system, focused on a thruster modulation problem and presented a practical solution to the problem of robust FDI for a satellite system Mars Express (MEX).…”

Interval observer design for Linear Parameter-Varying systems subject to component faults

“…In this case, observer design is usually based on the transformation of the system into canonical forms, which is difficult in practice [7]. This problem can be encountered in many industrial applications such as aircraft [1] [9], space vehicles [15] and wind turbine [19] [16]. To overcome this limitation, nonlinear systems can be represented by a Linear Parameter Varying (LPV) models.…”