Takahisa Kobayashi

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 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

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.

Integration of On-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management

Abstract This paper investigates the integration of on-line and off-line diagnostic algorithms for aircraft gas turbine engines. The on-line diagnostic algorithm is designed for in-flight fault detection. It continuously monitors engine outputs for anomalous signatures induced by faults. The off-line diagnostic algorithm is designed to track engine health degradation over the lifetime of an engine. It estimates engine health degradation periodically over the course of the engine’s life. The estimate generated by the off-line algorithm is used to “update” the on-line algorithm. Through this integration, the on-line algorithm becomes aware of engine health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. The benefit of this integration is investigated in a simulation environment using a nonlinear engine model. Introduction Early detection of faults is an important aspect in the health management of aircraft gas turbine engines. Undetected faults can lead the aircraft engine into an undesirable operating condition where compressor stall margin is reduced or turbine temperature is higher than the expected value. Under such a condition, safety and efficiency of engine operation may be compromised. Thus, it is critical to detect faults as early as possible and take the necessary corrective actions to avoid undesirable engine operation. To achieve a real-time fault detection capability, on-line diagnostic algorithms have been developed by several researchers (refs. 1 to 4). An on-line diagnostic algorithm is designed to run on an on-board engine computer in real time. It processes measured engine outputs to detect and, if possible, isolate a fault. Fault detection is pursued based on the fact that the measured engine outputs deviate from their reference condition values when an aircraft engine experiences a fault. An on-line diagnostic algorithm, however, encounters a challenge in achieving reliable performance. This challenge arises from the fact that the measured engine outputs are influenced not only by faults but also by engine health degradation. Engine health degradation is a normal aging process that all aircraft engines will experience due to usage and therefore is not considered as a fault, whereas a fault is an abnormal, unexpected event. Given the measured engine outputs, it is difficult to discern whether the engine output deviations are due to a fault or health degradation. Unless this issue is addressed, the on-line diagnostic algorithm will lose its effectiveness to detect faults as the engine degrades over its lifetime. One approach to address the above issue is to integrate the on-line diagnostic algorithm with an off-line trend monitoring algorithm. The task of an off-line trend monitoring algorithm is to track the engine health condition over the lifetime of an engine. It estimates the engine health condition based on steady-state engine output data recorded during flight (refs. 5 to 7). Since health degradation is a gradual process, the off-line algorithm needs to update its estimate at a relatively low frequency, such as once per a few flights or days. This periodically updated knowledge of engine health condition can be used to adjust the on-line diagnostic algorithm. Through this integration, the on-line diagnostic algorithm becomes aware of health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. In this paper, an on-line fault detection algorithm and an off-line trend monitoring algorithm are integrated, and the benefit of this integration is investigated in a simulation environment. The on-line fault detection algorithm is based on the hybrid Kalman filter (refs. 8 and 9), and the off-line trend monitoring algorithm is based on the extended Kalman filter (ref. 10). In the following sections, the details of each algorithm are described, followed by the application of the design methodology to a large commercial aircraft engine model. Then, the effectiveness of the integrated diagnostic approach is evaluated using simulated examples of health degradation and faults.

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 inflight 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.Copyright

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.