...Donald L. Simon; Jeffrey B. Armstrong; Sanjay Garg An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the under-determined estimation problem, in which...

... by implementing both the extended Kalman filter and the unscented Kalman filter as detection filters. Simulation results presented demonstrate the effectiveness of our proposed multiple model FDI algorithm for both structural and actuator faults in the jet engine. While the comparison between a single UKF...

... and saving costs, because unexpected stops can be avoided. In this paper, four different methodologies are applied to predict gas turbine behavior over time: Linear and Nonlinear Regression, One Parameter Double Exponential Smoothing, Kalman Filter and Bayesian Forecasting Method. The four methodologies...

...Donald L. Simon; Sanjay Garg A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown...

...S. Borguet; O. Léonard Kalman filters are widely used in the turbine engine community for health monitoring purposes. This algorithm has proven its capability to track gradual deterioration with good accuracy. On the other hand, its response to rapid deterioration is a long delay in recognizing...

...S. Borguet; P. Dewallef; O. Léonard Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction...

...S. Borguet; O. Léonard Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the discrepancies between the model prediction and the measurements are zero-mean, white random variables...

... be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters’ ability to facilitate matching of both measured and unmeasured engine outputs, as well as state...

...Takahisa Kobayashi; Donald L. Simon In this paper, a diagnostic system based on a uniquely structured Kalman filter is developed for its application to in-flight fault detection of aircraft engine sensors. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise...

... locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due...

...Takahisa Kobayashi; Donald L. Simon In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis...

...Dan Simon; Donald L. Simon Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based...

...P. Dewallef; C. Romessis; O. Léonard; K. Mathioudakis A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown...

... investigated ranging from the statistically based methods to those employing elements from the field of artificial intelligence. The two most publicized methods involve the use of either Kalman filters or artificial neural networks (ANN) as the primary vehicle for the fault isolation process. The present paper...

...N. Sugiyama System identification plays an important role in advanced control systems for jet engines, in which controls are performed adaptively using data from the actual engine and the identified engine. An identification technique for jet engine using the Constant Gain Extended Kalman Filter...