Prediction of the Remaining Useful Lifetime (RUL) of the system brings down the maintenance cost, downtime and also helps to take corrective measures. This results in avoiding catastrophic events. In this Paper, The RUL prediction is done based on KalmanFilter approach with dynamic curve fitting. The desired state (which is synthetic data) of the system is estimated based on a second order Kalman Filter, where Newtonian Kinematic model is used in tracking the significant feature state of the system. The dynamic curve fitting is done based on Least Square Error Sense method. The dynamically fitted curve is extrapolated until the failure threshold is reached and subsequently RUL is estimated. The algorithm thus developed is validated by a single phase full wave bridge rectifier analogous to aircraft Transformer Rectifier Unit (TRU), to obtain the real time significant feature data. The experimental results are compared with those of developed algorithms and results show Kalman filter based algorithm is ~95% accurate.
The ability to predict the aircraft fuel system health/operating condition and possible complications that occur during the long flight of an aircraft helps to improve the performance of the aircraft engine. Prognostics and Health Management (PHM) methodology includes fault detection, diagnosis, and prognosis. In this paper, we propose an Artificial Neural Network (ANN) based fault prognosis tool for a typical aircraft fuel system. Prognostics method using ANN's promise to provide a new approach to manage the fuel flow and fuel consumption of aircraft engine more effectively. This method identifies the presence of faults and mitigates them to maintain a proper fuel flow to the engine. Overlooking the presence of any faults in time could potentially be catastrophic which can lead to possible loss of lives and the aircraft as well. The developed tool works on the logical rules developed as per the engine's fuel consumption and quantity of fuel flow from the tanks. Here, we discuss the algorithm and the results of using ANN models to predict the health condition of the fuel system of aircraft.
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