Donald L. Simon

Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

ABSTRACTKalman ¯lters are often used to estimate the state variablesof a dynamic system. However, in the application of Kalman¯lters some known signal information is often either ignored ordealt with heuristically. For instance, state variable constraints(which may be based on physical considerations) are often ne-glected because they do not ¯t easily into the structure of theKalman ¯lter. This paper develops an analytic method of in-corporating state variable inequality constraints in the Kalman¯lter. The resultant ¯lter is a combination of a standard Kalman¯lter and a quadratic programming problem. The incorporationof state variable constraints increases the computational e®ort ofthe ¯lter but signi¯cantly improves its estimation accuracy. Theimprovement is proven theoretically and shown via simulationresults obtained from application to a turbofan engine model.This model contains 16 state variables, 12 measurements, and 8component health parameters. It is shown that the new algo-rithms provide improved performance in this example over un-constrained Kalman ¯ltering.INTRODUCTION

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

ABSTRACT In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated. NOMENCLATURE A16 Variable bypass duct area A8 Nozzle area BST Booster CLM Component Level Model FAN Fan FDI Fault detection and isolation FOD Foreign object damage HPC High-pressure compressor HPT High-pressure turbine LPT Low-pressure turbine P27 HPC inlet pressure PS15 Bypass duct static pressure PS3 Combustor inlet static pressure PS56 LPT exit static pressure T27D Booster inlet temperature T56 LPT exit temperature TMPC Burner exit heat soak WF36 Fuel flow XN2 Low-pressure spool speed, measured XN25 High-pressure spool speed, measured XNH High-pressure spool speed, state variable XNL Low-pressure spool speed, state variable

A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems

Abstract : Intelligent Control and Health Management technology for aircraft propulsion systems is much more developed in the laboratory than in practice. With a renewed emphasis on reducing engine life cycle costs, improving fuel efficiency, increasing durability and life, etc., driven by various government programs, there is a strong push to move these technologies out of the laboratory and onto the engine. This paper describes the existing state of engine control and on-board health management, and surveys some specific technologies under development that will enable an aircraft propulsion system to operate in an intelligent way-defined as self-diagnostic, self-prognostic, self-optimizing, and mission adaptable. These technologies offer the potential for creating extremely safe, highly reliable systems. The technologies will help to enable a level of performance that far exceeds that of todays propulsion systems in terms of reduction of harmful emissions, maximization of fuel efficiency, and minimization of noise, while improving system affordability and safety. Technologies that are discussed include various aspects of propulsion control, diagnostics, prognostics, and their integration. The paper focuses on the improvements that can be achieved through innovative software and algorithms. It concentrates on those areas that do not require significant advances in sensors and actuators to make them achievable, while acknowledging the additional benefit that can be realized when those technologies become available. The paper also discusses issues associated with the introduction of some of the technologies.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Takahisa KobayashiQSS Group, Inc., Brook Park, OhioDonald L. SimonU.S. Army Research Laboratory, Glenn Research Center, Cleveland, OhioPrepared for the37th Joint Propulsion Conference and Exhibitcosponsored by the AIAA, ASME, SAE, and ASEESalt Lake City, Utah, July 8-11, 2001National Aeronautics andSpace AdministrationGlenn Research Center

Constrained Kalman filtering via density function truncation for turbofan engine health estimation

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 on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This article develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the probability density function (PDF) of the Kalman filter estimate at the known constraints and then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variable constraints increases the computational effort of the filter but also improves its estimation accuracy. The improvement is demonstrated via simulation results obtained from a turbofan engine model. It is also shown that the truncated Kalman filter may provide a more accurate way of incorporating inequality constraints than other constrained filters (e.g. the projection approach to constrained filtering).

Development of an Information Fusion System for Engine Diagnostics and Health Management

Aircraft gas-turbine engine data is available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C-17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM * . Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

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. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well. a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDS system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a nonlinear simulation of a commercial aircraft gas turbine engine, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

Hybrid Kalman filter approach for aircraft engine in-flight diagnostics : Sensor fault detection case

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 linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the diagnostic system to be updated, through a relatively simple process, to the health condition of degraded engines. Through this health baseline update, the diagnostic effectiveness of the in-flight sensor fault detection system is maintained as the health of the engine degrades over time. The performance of the sensor fault detection system is evaluated in a simulation environment at several operating conditions during the cruise phase of flight.

eSTORM: Enhanced self tuning on-board real-time engine model

Abstract : A key to producing reliable engine diagnostics and prognostics resides in the fusion of different processing techniques. Fusion of techniques has been shown to improve diagnostic performance while simultaneously reducing false alarms. Presented here is an approach that fuses a physical model called STORM (Self Tuning Onboard, Real-time engine Model) developed by Pratt & Whitney, with an empirical neural net model to provide a unique hybrid model called enhanced STORM (eSTORM) for engine diagnostics. STORM is a piecewise linear approximation of the engine cycle deck. Though STORM provides significant improvement over existing real-time engine model methods, there are several effects that impact engine performance that STORM does not capture. Integrating an empirical model with STORM accommodates the modeling errors. This paper describes the development of eSTORM for a Pratt & Whitney high bypass turbofan engine. Results of using STORM and eSTORM on simulated engine data are presented and compared. eSTORM is shown to work extremely well in reducing STORM modeling errors and biases for the conditions considered.

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

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 parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.