Joint Kalman Filtering and Recursive Maximum Likelihood Estimation Approaches to Fault Detection and Identification of Boeing 747 Sensors and Actuators

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In this paper, a joint state and parameter estimation scheme is applied to address the problem of detection and identification of loss of effectiveness faults in both sensors or the actuators of a Boeing 747 longitudinal model. The Kalman filter and the recursive maximum likelihood schemes are used for the state and the parameter estimations, respectively. Compared to the other simultaneous state and parameter estimation methods, the proposed strategy maintains the linearity of the system and can also be applied to both sensor or actuator faults. In simulation studies conducted, our proposed approach is compared to the adaptive structure multiple-model scheme. In view of the computational resources considerations, the method proposed in this paper is more efficient than the adaptive structure multiple-model technique and also has the potential to detect and identify faults with lower severities as well as concurrent faults.