A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems
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paper_title: Fault Diagnosis based on Analytical Models for Linear and Nonlinear Systems - A Tutorial

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Abstract The diagnosis systems considered in this paper rely on the inconsistency between the actual process behaviour and its expected behaviour as described by an analytical model. The inconsistency is exhibited in signals called residuals. Two methods for residual generation are presented in a tutorial way: the parity space and the observer based approaches. Linear and nonlinear models are successively considered as a basis for the design of the residual generators.
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paper_title: Engine data analysis using decision trees

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Monitoring systems on aircraft engines typically record many parameters, sampled frequently, over the duration of a flight. On some newer engines such data have been recorded, and stored, over hundreds of flights. But data alone are not sufficient, and are too unwieldy, to help the analyst understand the state of the engine's health. Automated data mining, for knowledge discovery, is being successfully used in several fields. One well-known technique is a decision tree, which is induced from a subset of a known training set of engine data and outcome. Since engine parameters are continuous-valued a splitting criterion, known as the minimum description length principle, is used to define the branch points of the tree. The decision tree is induced from a subset of a known training set of engine data and outcome. Issues such as overfitting the training data, and the problem of large, or randomly-chosen, training sets are also discussed in this paper. Copyright ©2000 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. BACKGROUND Many airlines have invested in engine monitoring systems (EMS) on their newer aircraft. These systems record many parameters, such as temperatures, pressures, rotor speeds, fuel flow and others. The hope is that, by tracking the engines over hundreds of flights, one can discern component degradations which lead to diminished performance or safety. The large numbers of parameters, and the large volumes of time-series data, pose a challenge to the analyst sifting through the data. Simple checks such as minimum/maximum parameter bounds often lead to false alarms. Relaxing these bounds could lead to missed alerts. Several NASA Ames research efforts are directed at exploring ways of using computers to monitor the health of physical systems. In a previous paper [8] surveying aircraft engine health monitoring systems, various machine learning-based techniques, that are being researched for engine data analysis, were outlined. In this paper we focus on using the decision tree method for learning to classify engine's health based on its sensor data. (c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
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paper_title: A SURVEY OF AIRCRAFT ENGINE HEALTH MONITORING SYSTEMS

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This paper presents a survey of engine health monitoring systems for commercial aircraft. The state of practice is explored first, with the purpose of identifying the shortcomings of current systems. The state of the research to address these shortcomings is them surveyed to explore the alternatives. Research and monitoring applications for various other types of engines provide a good basis for further exploring the topic. This survey is meant to serve as a precursor to engine health and monitoring research at the NASA Ames Research Center.
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paper_title: Fundamentals of model-based diagnosis

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Abstract Over the last 25 years, the Computer Science community and particularly the Artificial Intelligence community, have developed a framework for system diagnosis, called Model-Based Diagnosis. This framework is extremely general and covers a broad range of capabilities including detecting malfunctions, isolating faulty components, handling multiple faults, identifying repair actions, and automatically generating embedded software. This field grew independently of the fault detection and isolation community (FDI) and has developed its own terminologies and conventions. This paper is an attempt to present the fundamental concepts of Model-Based Diagnosis (MBD) in onc place and in one consistent terminology, and thus make the field much more accessible to the FDI community.
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paper_title: Optimal discrete event supervisory control of aircraft gas turbine engines

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This paper presents an application of the recently developed theory of optimal discrete event supervisory (DES) control that is based on a signed real measure of regular languages. The DES control techniques are validated on an aircraft gas turbine engine simulation test bed. The test bed is implemented on a networked computer system in which two computers operate in the client-server mode. Several DES controllers have been tested for engine performance and reliability.
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paper_title: Pilot's Associate: a cooperative, knowledge-based system application

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The Pilot's Associate program, a joint effort of the Defense Advanced Research Projects Agency (DARPA) and the US Air Force to build a cooperative, knowledge-based system to help pilots make decisions is described, and the lessons learned are examined. The Pilot's Associate concept developed as a set of cooperating, knowledge-based subsystems: two assessor and two planning subsystems, and a pilot interface. The two assessors, situation assessment and system status, determine the state of the outside world and the aircraft systems, respectively. The two planners, tactics planner and mission planner, react to the dynamic environment by responding to immediate threats and their effects on the prebriefed mission plan. The pilot-vehicle interface subsystem provides the critical connection between the pilot and the rest of the system. The focus is on the air-to-air subsystems. >
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paper_title: Sensor Needs for Control and Health Management of Intelligent Aircraft Engines

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ABSTRACT NASA and the U.S. Department of Defense are conducting programs which support the future vision of “intelligent” aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized.
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paper_title: A Study on the Requirements for Fast Active Turbine Tip Clearance Control Systems

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This paper addresses the requirements of a control system for active turbine tip clearance control in a generic commercial turbofan engine through design and analysis. The control objective is to articulate the shroud in the high pressure turbine section in order to maintain a certain clearance set point given several possible engine transient events. The system must also exhibit reasonable robustness to modeling uncertainties and reasonable noise rejection properties. Two actuators were chosen to fulfill such a requirement, both of which possess different levels of technological readiness: electrohydraulic servovalves and piezoelectric stacks. Identification of design constraints, desired actuator parameters, and actuator limitations are addressed in depth; all of which are intimately tied with the hardware and controller design process. Analytical demonstrations of the performance and robustness characteristics of the two axisymmetric LQG clearance control systems are presented. Takeoff simulation results show that both actuators are capable of maintaining the clearance within acceptable bounds and demonstrate robustness to parameter uncertainty. The present model-based control strategy was employed to demonstrate the tradeoff between performance, control effort, and robustness and to implement optimal state estimation in a noisy engine environment with intent to eliminate ad hoc methods for designing reliable control systems.
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paper_title: An investigation of life extending control techniques for gas turbine engines

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The consumption of engine life characterized by low EGT margin, expended life-limited parts, and slow engine accelerations is the principal cause of aircraft engine removal. Life extending control results from a conscious effort on the part of control system designers to extend the life of an engine by modifying the control logic or control hardware to influence one or more of these life-consuming factors. General Electric Aircraft Engines and NASA Glenn Research Center are currently engaged in a collaborative research programme to investigate control technologies applicable to extending on-wing life of aircraft engines. A trade study of potential schemes that may have a positive impact on engine life has been performed, and the results of this study are used to narrow the focus of further research under this programme.
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paper_title: Intelligent Life -Extending Controls for Aircraft Engines

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Aircraft engine controllers are designed and operated to provide desired performance and stability margins. The purpose of life -extending -control (LEC) is to study the relationship between control action and engin e component life usage, and to design an intelligent control algorithm to provide proper trade -offs between performance and engine life usage. The benefit of this approach is that it is expected to maintain safety while minimizing the overall operating co sts. With the advances of computer technology, engine operation models, and damage physics, it is necessary to reevaluate the control strategy for overall operating cost consideration. This paper uses the thermo -mechanical fatigue (TMF) of a critical com ponent to demonstrate how an intelligent engine control algorithm can drastically reduce the engine life usage with minimum sacrifice in performance. A Monte Carlo simulation is also performed to evaluate the likely engine damage accumulation under variou s operating conditions. The simulation results show that an optimized acceleration schedule can provide a significant life saving in selected engine components. Nomenclature
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paper_title: Model-Based Life Extending Control for Aircraft Engines

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** * * The objective of this work is to extend the on-wing life of aircraft engines using advanced control techniques. Previous studies demonstrated that, out of various schemes that have a positive impact on engine life, improved active clearance control (ACC) is the one with the best potential. Modern commercial turbofan engines are already implementing various forms of ACC schemes to maintain turbine tip clearances within appropriate ranges for safety and efficiency reasons. However, one limitation of these schemes is that they are designed for a "nominal" engine, and as the engine deteriorates over time the ACC performance degrades. The present paper proposes to improve the high-pressure turbine ACC in a high bypass commercial engine to account for deterioration. A key component of this new ACC scheme is an estimator of the clearance variation due to deterioration. This estimator processes the difference between some of the actual engine outputs and outputs from a model of the nominal engine running in real-time. The new ACC scheme was implemented on a high-fidelity model of the commercial engine. Extensive simulations show that exhaust gas temperatures are clearly lowered by the new ACC, which translate into more cycles between engine overhauls.
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paper_title: Longitudinal emergency control system using thrust modulation demonstrated on an MD-11 airplane

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This report describes how an MD-11 airplane landed using only thrust modulation, with the control surfaces locked. The propulsion-controlled aircraft system would be used if the aircraft suffered a major primary flight control system failure and lost most or all the hydraulics. The longitudinal and lateral–directional controllers were designed and flight tested, but only the longitudinal control of flightpath angle is addressed in this paper. A flight-test program was conducted to evaluate the aircraft’s high-altitude flying characteristics and to demonstrate its capacity to perform safe landings. In addition, over 50 low approaches and three landings without the movement of any aerodynamic control surfaces were performed. The longitudinal control modes include a wing engines only mode for flightpath control and a three-engine operation mode with speed control and dynamic control of the flightpath angle using the tail engine. These modes were flown in either a pilot-commanded mode or an instrument landing system coupled mode. Also included are the results of an analytical study of an autothrottle longitudinal controller designed to improve the phugoid damping. This mode requires the pilot to use differential throttles for lateral control. Nomenclature Alon longitudinal state derivative matrix Blon control input derivative matrix c.g. center of gravity *Aerospace Engineer. †Chief, Propulsion Branch. Associate Fellow AIAA. ‡Flight Control Engineer. Copyright  1996 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. 1 American Institute of Aero Clon state output matrix Dlon control input observation matrix EPR engine pressure ratio (turbine and inlet total pressures) FADEC full-authority digital engine control computers FCC flight control computer FCP flight control panel sink rate, ft/sec ILS instrument landing system flightpath error feed-forward gain, deg pitch integrator error gain, 1/sec pitch rate feedback gain, deg/deg/sec velocity error feedback gain, deg/kn pitch angle feedback gain, deg/deg/sec center engine washout gain, lb MCDU multifunction control and display unit PCA propulsion-controlled aircraft PIO pilot induced oscillation q pitch rate, deg/sec t time, sec uu x axis velocity perturbation, ft/sec Vel velocity or airspeed, kn s Laplace transform ww z axis velocity perturbation, ft/sec xlon longitudinal state vector α angle of attack, deg γ flightpath angle, deg ḣ Kvc Kvi Kq K rs sec Kthad Kvm nautics and Astronautics flightpath angle command, deg velocity error θ pitch attitude, deg pitch attitude rate, deg/sec φ roll attitude, deg Introduction Aircraft flight control systems are designed with extensive redundancy to ensure a low probability of failure. During recent years, however, several aircraft have experienced major flight control system failures, leaving engine thrust as the only control effectors.1,2 In some of these emergency situations, the engines were used to maintain control of the airplane flightpath angle, γ. In the majority of the cases surveyed, crashes resulted, and over 1200 people have died.1 The challenge was to create a sufficient degree of control through thrust modulation to control and safely land an airplane with severely damaged or inoperative flight control surfaces. Meeting this challenge is the objective of the Propulsion-Controlled Aircraft (PCA) Emergency Backup System. The PCA emergency backup flight control system requires that the airplane have at least two engines, preferably two wing engines. In addition, the normal control surfaces can not be locked in a hardover position which could exceed the moments resulting from the thrust of the engines. The National Aeronautics and Space Administration, Dryden Flight Research Center, Edwards, California, has performed nonlinear and linear analytical studies and conducted several flight-test programs investigating the PCA concept. Results of these programs2–6 show that gross control can be obtained by manually moving the throttles. However, making a safe runway landing is exceedingly difficult because of low phugoid and dutch roll damping coupled with the high pilot work load near the ground. To improve the performance and reduce the pilot work load, the PCA program was developed. The goal was to make flying an airplane with the PCA system a viable task with minimal or no previous pilot training with this system. This report describes the longitudinal PCA control systems and flight test results of four modes: • Mode A—using the wing engines only for control of flightpath angle, γ. • Mode B—using the tail engine for speed control in conjunction with mode A. • Mode C—using all the wing and tail engines for dynamic control of γ and speed control. • Mode D—using an existing autothrottle system for γ control. The autothrottle system was developed to provide a simpler implementation that did not require changes to the engine controllers. This system was not flight tested, but simulation results are presented.§ Within control modes A, B, and C, the pilot has the option of selecting the instrument landing system (ILS)coupled with PCA for approach and landing. This option virtually eliminates the pilot work load. Two ILS landings using the wing engines (mode A) were performed, and one is presented in this report. The lateral–directional controller is described in reference 7. Test Vehicle Description The MD-11 airplane is a large, long-range, threeengine, wide-body transport. This airplane is 202 ft long, has a wing span of 170 ft, and a maximum takeoff gross weight of 618,000 lb (fig. 1). Flight Control Systems The MD-11 airplane has a mechanical flight control system with irreversible hydraulically powered actuators. The hydraulic power provided by three independent systems is intended for fail-safe capability. Essential control functions may be maintained by any one of these three systems. Pitch control is provided by dual elevators on each horizontal stabilizer, and pitch trim is provided by a moveable horizontal stabilizer. Inboard and outboard ailerons supplemented by wing spoilers provide roll control. A dual rudder mounted on a single vertical stabilizer provides yaw control. The lateral dynamics is controlled by the yaw damper. The longitudinal stability augmentation system controls the pitch dynamics. The aerodynamic surfaces are controlled by hydraulic actuators. The flight control computers (FCC) were built by Honeywell, Phoenix, Arizona, and operate at 20 samples/sec. The MD-11 airplane is equipped with a flight management system which integrates autopilot, navigation, and autoland functions. The automatic pilot control includes a thumbwheel for commanding flightpath angle, . §NASA has a patent pending for mode d. γ cmd γ err
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paper_title: Fuzzy Fuel Flow Selection Logic for a Real Time Embedded Full Authority Digital Engine Control

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The control logic of the modern full authority digital engine control is comprised of many control loops, each of which has a specific purpose. Typical control loops include (but are not limited to) a high or low rotor speed governor, an acceleration and deceleration loop, and various limiting loops for temperature, speed, fuel flow, and rate of change of fuel flow. The logic that determines which of these loops is in control at any particular time has a history of being very simplistic. This selection logic is usually nothing more that a cascade of minimum and maximum selection logic gates. Since this logic is so simplistic, the control engineer often has to fine-tune the compensator design for each loop, in order to achieve proper performance.
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paper_title: Survivable Engine Control Algorithm Development (SECAD)

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This presentation describes the work conducted as part of the Survivable Engine Control Algorithm Development (SECAD) project. The SECAD project was sponsored by the Joint Technical Coordinating Group on Aircraft Survivability (JTCG/AS) Vulnerability Reduction Subgroup and conducted by the Naval Air Warfare Center Weapons Division and General Electric Aircraft Engines, Lynn MA. The overall objective of the SECAD project is to develop turbine engine control algorithms that have the potential to reduce aircraft engine vulnerability to combat damage, including foreign object damage (FOD). The initial development effort was laid out in three-phases. Phase 1 conducted in early FY99, defined candidate engine damage modes to be addressed, and approaches to mitigate the damage effects. In phase 2 damage models, detection algorithms, and mitigation strategies were developed and evaluated using F414 computer engine models. Phase 3 integrated the damage detection algorithms with the F414 control system and an engine test was conducted to demonstrate the capabilities of the concepts developed. During these tests, SECAD successfully detected fan and compressor damage, combustor airflow loss and variable exhaust nozzle loss of control. This presentation documents the successful development and test of the damage detection and mitigation algorithms, and discusses the potential for merging these techniques with current prognostics and health monitoring technologies.
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paper_title: Adaptive Control

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From the Publisher: ::: Written by two of the pioneers in the field, this book contains a wealth of practical information unavailable anywhere else. The authors give a comprehensive presentation of the field of adaptive control, carefully bending theory and implementation to provide the reader with insight and understanding. Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.
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paper_title: Multi-Variable Control of the GE T700 Engine using the LQG/LTR Design Methodology

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In this paper we examine the design of scalar and multi-variable feedback control systems for the GE T700 turboshaft engine coupled to a helicopter rotor system. A series of linearized models are presented and analyzed. Robustness and performance specifications are posed in the frequency domain. The LQG/LTR methodology is used to obtain a sequence of three feedback designs. Even in the single-input single-output case, comparison of the current control system with that derived from the LQG/LTR approach shows significant performance improvement. The multi-variable designs, evaluated using linear and nonlinear simulations, show even more potential for performance improvement.
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paper_title: Performance benefits of adaptive in-flight propulsion system optimization

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The communication throughput and data-processing capacities of integrated flight/propulsion control systems allow engine operating schedules to be adjusted in-flight, on the basis of adaptive optimization algorithms which identify engine component performance variations due to manufacturing, wear, and damage. A quantification is presently made of the performance benefits accruing to adaptive in-flight optimization, via comparisons of fuel consumption and turbine temperature data for variable geometry and component match optimized cases with conventional cases. A low-bypass mixed-flow turbofan and a high-bypass nonmixed turbofan are thus treated. 6 refs.
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paper_title: MODEL-BASED INTELLIGENT DIGITAL ENGINE CONTROL (MoBIDEC)

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A model-based control system for an advanced gas turbine engine is described, and some preliminary performance benefits are derived based on simulation testing. The approach to plant model development and a method for adapting a nominal engine model to reflect the performance of a deteriorated engine is given. A general process for model-based control law design and validation, with emphasis on linear and nonlinear analysis, is then presented. Performance and Operability modes that implement closed loop control of model-derived parameters are demonstrated via simulation testing. Future plans include testing of the model-based control described in this paper on an IHPTET Phase II advanced technology engine. NOMENCLATURE
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paper_title: eSTORM: Enhanced self tuning on-board real-time engine model

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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.
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paper_title: Performance-analysis-based gas turbine diagnostics: A review

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Abstract Gas turbine diagnostics has a history almost as long as gas turbine development itself. Early engine fault diagnosis was carried out based on manufacturer information supplied in a technical manual combined with maintenance experience. In the late 1960s, when L. A. Urban introduced gas path analysis, gas turbine diagnostics made a big breakthrough. Since then different methods have been developed and used in both aerospace and industrial applications. To date, a substantial number of papers have been published in this area. This paper intends to give a comprehensive review of performance-analysis-based methods available thus far for gas turbine fault diagnosis in the open literature.
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paper_title: Sensor biases effect on the estimation algorithm for performance-seeking controllers

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The performance-seeking-control algorithm (PSC) is designed to continuously optimize the performance of propulsion systems. The PSC uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters (EDPs) characterizing engine deviations with respect to nominal conditions. In practice, the measurement biases (or model uncertainties) may prevent the estimated EDPs from reflecting the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. An observability analysis shows that the biases cannot be estimated together with the EDPs. Moreover, biases and EDPs turn out to have equivalent effects on the measurements, leaving it undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. In this article, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine.
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paper_title: Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

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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
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paper_title: Turbofan engine demonstration of sensor failure detection

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In the paper, the results of a full-scale engine demonstration of a sensor failure detection algorithm are presented. The algorithm detects, isolates, and accommodates sensor failures using analytical redundancy. The experimental hardware, including the F100 engine, is described. Demonstration results were obtained over a large portion of a typical flight envelope for the F100 engine. They include both subsonic and supersonic conditions at hoth medium and full, nonafter burning, power. Estimated accuracy, minimum detectable levels of sensor failures, and failure accommodation performance for an F100 turbofan engine control system are discussed. HE objective of the advanced detection, isolation, and accommodation (ADIA) program was to improve demonstrated reliability of digital electronic control systems for turbine engines by detecting, isolating, and accommodat- ing sensor failures using analytical redundancy methods. This paper discusses the results of an engine demonstration of an analytical-redundancy-based algorithm developed as part of the ADIA program. Over the past 45 years, hydromechanical implementations of turbine engine control systems have ma- tured into highly reliable units. However, there is a trend toward increased engine complexity. Engine control has be- come increasingly complex and has evolved from a hydrome- chanical to a full authority digital electronic (FADEC) imple- mentation. These FADEC-type controls have to demonstrate the same or improved levels of reliability as their hydrome- chanical predecessors. Various redundancy management techniques have been ap- plied to both the total control system and to individual compo- nents. Studies1 have shown that the least reliable of the control system components are the engine sensors. Sensor redundancy will be required to achieve adequate control system reliability. One important type of sensor redundancy is analytical redun- dancy2 (AR), which uses a model to generate redundant infor- mation that can be compared to measured information to detect failures. AR-based systems can have cost and weight savings over hardware redundancy. Considerable progress has been made in the application of analytical redundancy to improve turbine engine control sys- tem reliability. However, little has been done to demonstrate AR-based techniques on real engine systems. One exception was a flightiest program3 where selected out-of-range (hard) sensor faults were induced and the resulting actions of the control evaluated. The test program included a ground-thrust stand evaluation and a flight test. The sensors that failed during the flight test included the compressor inlet variable geometry sensor, inlet static pressure, burner pressure, and fan turbine inlet temperature. Most failures were detected and accommodated. However, a recreation of a broken line (hard) burner pressure went undetected. Pilot response to aircraft performance after accommodation was favorable.
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paper_title: Using neural networks for sensor validation

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This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.
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paper_title: Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

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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 FDI 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 commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.Copyright © 2004 by ASME
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paper_title: Neural network-based sensor validation for turboshaft engines

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Abstract : Sensor failure detection, isolation, and accommodation using a neural network approach is described. An autoassociative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.
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paper_title: Sensor and Data Fusion Concepts and Applications

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Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation Data Fusion Algorithms and Architectures Bayesian Inference Dempster-Shafer Algorithm Artificial Neural Networks Voting Fusion Fuzzy Logic and Neural Networks Passive Data Association Techniques for Unambiguous Location of Targets. Appendices: Planck Radiation Law and Radiative Transfer Voting Fusion With Nested Confidence Levels.
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paper_title: Development of an information fusion system for engine diagnostics and health management

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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.
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paper_title: Fuzzy belief networks

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The most common method for knowledge representation in an expert system is the production rule [Waterman 1986]. Unfortunately, the modularity inherent in a rule-based system is limiting, especially in an uncertain environment [Morawski 1989]. A fuzzy belief network (FBN) provides a more holistic, graphical approach and lends itself well to implementation in expert systems on personal and small computers.
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paper_title: Bayesian belief networks for fault identification in aircraft gas turbine engines

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Describes the methodology for usage of Bayesian belief networks (BBNs) in fault detection for aircraft gas turbine engines. First, the basic theory of BBNs is discussed, followed by a discussion on the application of this theory to a specific engine. In particular, the selection of faults and the means by which operating regions for the BBN system are chosen are analyzed. This methodology is then illustrated using the GE CFM56-7 turbofan engine as an example.
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paper_title: PCA-based Fuzzy Classification of Turbine Blade Fatigue Modes

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An efficient classification method is needed for th e health diagnosis of turbine blades. Traditional classification methods assign each dat a point or feature vector to one and only one of the categories, which poses significant prob lems in dealing with the data set containing overlapping structures. In this paper, a PCA-based fuzzy classification method is used to address this problem. After preprocessing o f the data, Principle Component Analysis (PCA) is first used to reduce the excessive dimensi onality in the feature space. Generalized multi-dimension Gaussian membership functions are then formulated for each category among the training patterns. Finally every new patt ern can be assigned appropriate membership value for each category. The proposed algorithm has been tested using a set of historical flight data from a group of engines. Exp erimental results have shown that this approach is effective in classifying turbine blade fatigue modes and agrees with the manual inspection findings.
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Title: A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems
Section 1: Introduction
Description 1: Write an introductory section that outlines the paper’s intent, general background, and organization. 

Section 2: Engine Overview
Description 2: Provide a detailed explanation of the turbofan engine components, their function, and their operational relevance.

Section 3: Control
Description 3: Discuss traditional and modern control techniques for aircraft engines, including specific applications and approaches beyond current practices.

Section 4: Diagnostics
Description 4: Explore various systems and approaches for on-wing health monitoring, including sensor validation and fault detection.

Section 5: Intelligent Control
Description 5: Explain modern intelligent control techniques and their potential to enhance aircraft propulsion system performance.

Section 6: Life Extending Control
Description 6: Describe life-extending control techniques that minimize component damage while maintaining acceptable engine response.

Section 7: Adaptive Control
Description 7: Discuss adaptive control techniques that adjust controller gains and schedules based on operating conditions.

Section 8: Multivariable Control
Description 8: Review multivariable control schemes emphasizing the optimization of system performance through state space representation.

Section 9: Performance Seeking Control
Description 9: Explain the concept and implementation of performance seeking control, aiming at optimizing engine operation based on current conditions.

Section 10: Model Predictive Control
Description 10: Describe model predictive control (MPC), an open loop control scheme that uses real-time models for optimization.

Section 11: Gas Path Performance Diagnostics
Description 11: Outline the techniques for on-line monitoring of gas path components and the challenges in estimating health parameters.

Section 12: Vibration Diagnostics
Description 12: Discuss the role of vibration diagnostics in assessing structural health and future advancements in high frequency measurements.

Section 13: Lubrication System Diagnostics
Description 13: Review the techniques used for monitoring lubrication systems and identifying part wear through contaminants.

Section 14: Control System Integrity Assurance
Description 14: Explore methods for ensuring the integrity of control systems through various diagnostic sensors and validation techniques.

Section 15: Diagnostic Sensors
Description 15: Discuss the types of sensors employed for propulsion diagnostics and the challenges associated with integrating new sensors.

Section 16: Data/Information Fusion
Description 16: Explain the application of data fusion techniques to integrate data from multiple sources for enhanced engine health management.

Section 17: Prognostics
Description 17: Describe prognostic methods for estimating the remaining useful life of engine components based on operating conditions.

Section 18: Integration
Description 18: Discuss the integration of various algorithms and systems into a cohesive engine health management system.

Section 19: Integrated Fault Detection, Isolation, and Accommodation Logic
Description 19: Elaborate on FDIA logic embedded within electronic engine controllers to detect and mitigate faults in real-time.

Section 20: MPC for Integrated Control and Diagnostics
Description 20: Explain how MPC can integrate control and diagnostics by accounting for faults within its optimization framework.

Section 21: Engine Health Management, Support Engineering, Maintenance and Logistics Integration
Description 21: Discuss the potential of automated engine health management to reduce maintenance effort and improve logistical processes.

Section 22: Final Comments
Description 22: Conclude the paper with final remarks on the potential contributions of intelligent control and health management technologies to future engines.