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The objective of this paper is to develop a hybrid scheme (nonlinear observer and fuzzy decision making) for fault detection and isolation (FDI) in the reaction wheels of a satellite. Specifically, the goal is to decide whether a bus voltage fault, a current fault or a temperature fault has occurred in one of the three reaction wheels and further to localize which fault has occurred. In order to achieve these objectives, high fidelity model is used to exhibit the dynamics of the wheels on each of the three axes independently. First using the dynamic equations, nonlinear observer is designed, and then comparing estimated and actual states, residual signals are generated. These two input signals comprise the decision making unit. Design of the decision making unit using fuzzy reasoning is implemented. The effectiveness of this nonlinear-fuzzy based FDI scheme is investigated and a comparative study is conducted with the performance of a generalized observer-based scheme. [ABSTRACT FROM AUTHOR]

Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Hard failures are out-of range or large bias errors that occur instantaneously in the sensed values. The objective of the hard-failure diagnosis algorithm is to improve the overall demonstrated reliability of engine control system by using analytical redundancy to detect sensor failures. A new self-tuning Kalman filter based on engine model has been designed to avoid the flaw of classical Kalman filter which needs to accurately know the model parameter and statistical characteristic of noise in system. The self-tuning Kalman filter has been used in hard-failure and simulations have been done in fault sensors in the paper. From the simulation results, the conclusion can be deduced that the fault diagnosis method designed using self-tuning Kalman filter can detect sensor failures successfully, isolate sensed signal, and accommodate signal timely. [ABSTRACT FROM AUTHOR]

Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger. [ABSTRACT FROM AUTHOR]

An unknown input high gain observer based parametric fault detection and isolation scheme is presented. First, a reduced order unknown input high gain observer is derived. Then, using these observers, a fault detection and isolation technique is devised to detect and isolate the parametric fault of a system whose parameters are uncertain to some extent. The proposed FDI algorithm consists of two steps. In the first step, the detection of fault and the isolation of faulty region are achieved and in the next step, the faulty parameter is isolated from the faulty region. Effectiveness of the observer as well as the FDI technique is shown with the help of a numerical example. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for satellites that are tasked to perform a formation flying mission. Specifically, the proposed FDI scheme is developed for Pulsed Plasma Thrusters (PPT) that are considered to be used in satellite΄s Attitude Control Subsystem (ACS). By using the relative attitudes of satellites in the formation our proposed ˵High Level″ fault diagnoser scheme can detect a pair of thrusters that are faulty. This high level diagnoser however cannot isolate the faulty satellite in the formation. Towards this end, a novel ˵Integrated″ dynamic neural network (DNN)-based FDI scheme is proposed to achieve both fault detection and fault isolation of the formation flying of satellites. This methodology involves an ˵optimal″ fusion of the ˵High Level″ FDI scheme with a DNN-based ˵Low Level″ FDI scheme that was recently developed by the authors. To demonstrate the FDI capabilities of our proposed schemes various fault scenarios are simulated and a comparative study among the techniques is performed. [ABSTRACT FROM AUTHOR]