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Author: Wei Xue
Publisher:
ISBN: 9789533070940
Category :
Languages : en
Pages :
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Book Description
In this paper, aircraft engine sensor fault diagnostics based on the estimation of health degradation was investigated. The tracking filter estimates engine health condition over the course of engine's life. Through this integration, the on-line fault detection algorithm is able to maintain its diagnostic effectiveness as the aircraft engine degrades over its lifetime. The integrated approach was investigated in a simulation environment using a nonlinear engine model. The evaluation result showed that this approach is essential to maintain online fault detection capability in the presence of health degradation. In this paper, an approach has been proposed to detect and isolate the aircraft sensor and actuator failures occurred in the aircraft control system. A bank of Kalman filters were used to detect and isolate sensor failures, each of Kalman filter is designed based on a specific hypothesis for detecting a specific sensor fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. Failures in the sensors and actuators affect the characteristics of the residual signals of the Kalman filter. When the Kalman filter is used, the decision statistics changes regardless the faults in the sensor or in the actuator. While a Robust Kalman filter is used, it is easy to distinguish the sensor and actuator fault.
Author: Wei Xue
Publisher:
ISBN: 9789533070940
Category :
Languages : en
Pages :
Get Book Here
Book Description
In this paper, aircraft engine sensor fault diagnostics based on the estimation of health degradation was investigated. The tracking filter estimates engine health condition over the course of engine's life. Through this integration, the on-line fault detection algorithm is able to maintain its diagnostic effectiveness as the aircraft engine degrades over its lifetime. The integrated approach was investigated in a simulation environment using a nonlinear engine model. The evaluation result showed that this approach is essential to maintain online fault detection capability in the presence of health degradation. In this paper, an approach has been proposed to detect and isolate the aircraft sensor and actuator failures occurred in the aircraft control system. A bank of Kalman filters were used to detect and isolate sensor failures, each of Kalman filter is designed based on a specific hypothesis for detecting a specific sensor fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. Failures in the sensors and actuators affect the characteristics of the residual signals of the Kalman filter. When the Kalman filter is used, the decision statistics changes regardless the faults in the sensor or in the actuator. While a Robust Kalman filter is used, it is easy to distinguish the sensor and actuator fault.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
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. (7 tables, 4 figures, 17 refs.).
Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781720375425
Category :
Languages : en
Pages : 40
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Book Description
In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis 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, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.Kobayashi, Takahisa and Simon, Donald L. (Technical Monitor)Glenn Research CenterAIRCRAFT ENGINES; FAULT DETECTION; KALMAN FILTERS; SYSTEMS HEALTH MONITORING; SENSORS; ACTUATORS; COMPONENT RELIABILITY; NONLINEARITY; COMPUTERIZED SIMULATION; DEGRADATION; ENGINE PARTS; ERROR ANALYSIS; PROPULSION SYSTEM PERFORMANCE; STEADY STATE
Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781721832293
Category :
Languages : en
Pages : 26
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Book Description
In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. 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 in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated. Kobayashi, Takahisa and Simon, Donald L. Glenn Research Center NASA/TM-2006-214491, E-15783, ARL-TR-4001
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 44
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Book Description
Author: Constantin Volosencu
Publisher: BoD – Books on Demand
ISBN: 1789844363
Category : Mathematics
Languages : en
Pages : 130
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Book Description
This book offers a selection of papers in the field of fault detection and diagnosis, promoting new research results in the field, which come to join other publications in the literature. Authors from countries of four continents: United States of America, South Africa, China, India, Algeria and Croatia published worked examples and case studies resulting from their research in the field. Fault detection and diagnosis has a great importance in all industrial processes, to assure the monitoring, maintenance and repair of the complex processes, including all hardware, firmware and software. The book has four sections, determined by the application domain and the methods used: 1. Hybrid Computing Systems, 2. Power Systems, 3. Power Electronics and 4. Kalman Filtering. In the first section, the readers will find a technical report on fault diagnosis of hybrid computing systems, based on the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments. In the second section, two chapters are included: one of them presents a study on preventive maintenance and fault detection for wind turbine generators using statistical models and the second chapter presents a technical report on fault diagnosis for turbo-generators, based on the mechanical-electrical intersectional characteristics. The third section contains a technical report that presents some techniques of detection and localization of open-circuit faults in a three-phase voltage source inverter fed induction motor. The fourth section presents a theoretical study on the application of distributed discrete-time linear Kalman filtering with decentralized structure of sensors in fault residual generation.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Book Description
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 paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satis- fied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estima- tion accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model con- tains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.
Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781721669110
Category :
Languages : en
Pages : 32
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Book Description
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. Kobayashi, Takahisa and Simon, Donald L. Glenn Research Center NASA/TM-2004-213203, ARL-TR-3252, GT2004-53640, E-14712
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
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.
Author: National Aeronautics and Space Adm Nasa
Publisher: Independently Published
ISBN: 9781723734168
Category : Science
Languages : en
Pages : 38
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Book Description
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 paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.Simon, Dan and Simon, Donald L.Glenn Research CenterTURBOFAN ENGINES; AIRCRAFT ENGINES; KALMAN FILTERS; QUADRATIC PROGRAMMING; SYSTEMS HEALTH MONITORING; GAS TURBINE ENGINES; ALGORITHMS; ESTIMATES; INEQUALITIES; SIMULATION
