A model based Vehicle Health Monitoring system for the Space Shuttle Main Engine (original) (raw)
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IEEE Transactions on Control Systems Technology, 2015
In this paper, a novel sensor fault detection, isolation and identification (FDII) strategy is proposed by using the multiple model (MM) approach. The scheme is based on multiple hybrid Kalman filters (HKF) which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed multiple HKF-based FDI scheme is extended to identify the magnitude of a sensor fault by the use of a modified generalized likelihood ratio (GLR) method which relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent faulty sensor scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical multiple HKF-based fault detection, isolation and identification scheme under different flight modes. Finally, our proposed HKF-based FDI approach is compared with various filtering methods such as the linear, extended, unscented and cubature Kalman filters (LKF, EKF, UKF and CKF, respectively) corresponding to both interacting and non-interacting multiple model (MM) based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarms, as well as robustness with respect to the engine health parameters degradations.
Diagnostic models for sensor measurements in rocket engine tests
2009 IEEE Sensors, 2009
This paper presents our ongoing work in the area of using virtual reality (VR) environments for the Integrated Systems Health Management (ISHM) of rocket engine test stands. Specifically, this paper focuses on the development of an intelligent valve model that integrates into the control center at NASA Stennis Space Center. The intelligent valve model integrates diagnostic algorithms and 3D visualizations in order to diagnose and predict failures of a large linear actuator valve (LLAV). The diagnostic algorithm uses auto-associative neural networks to predict expected values of sensor data based on the current readings. The predicted values are compared with the actual values and drift is detected in order to predict failures before they occur. The data is then visualized in a VR environment using proven methods of graphical, measurement, and health visualization. The data is also integrated into the control software using an ActiveX plug-in.
Rocket engine failure detection using system identification techniques
26th Joint Propulsion Conference, 1990
This paper presents the theoretical foundation and application of two univariate failure detection algorithms to Space Shuttle Main Engine (SSME) test firing data. Both algorithms were applied to data collected during steady-state operation of the engine. One algorithm, the time series algorithm, is based on time series techniques and involves the computation of autoregressive models. Time series techniques have been previously applied to SSME data. The second algorithm is based on standard signal processing techniques. It consists of tracking the variations in the average signal power with time. The average signal power algorithm is a newly proposed SSME failure detection algorithm. Seven nominal test firings were used to develop failure indication thresholds for each algorithm. These thresholds were tested using four anomalous firings aud one additional nominal firing. Both algorithms provided significantly earlier failure indication times than did the current redline limit system. Neither algorithm gave false failure indications for the nominal firing. The strengths and weaknesses of, the two algorithms are discussed and compared. The average signal power algorithm was found to have several advantages over the time series algorithm. AR ARMA CADS e(i)
Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
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.
Statistical estimation of multiple faults in aircraft gas turbine engines
2009
This article presents estimation of multiple faults in aircraft gas-turbine engines, based on a statistical pattern recognition tool called symbolic dynamic filtering. The underlying concept is formulated by statistical analysis of evidences to estimate anomalies (i.e. deviations from the nominal values) in multiple critical parameters of the engine system; it also presents a framework for sensor information fusion. The fault estimation algorithm is validated on a numerical simulation test-bed that is built upon the NASA C-MAPSS model of a generic commercial aircraft engine. a statistical sense. Given possible statistics of future operating conditions and an expected component deterioration profile, the role of prognosis is to estimate the remaining life of the engine from the information generated in the diagnosis step. Nevertheless, an aircraft engine is a complex large-scale dynamical system whose subsystems are interconnected physically as well as through feedback control loops. It is a challenging task to detect, isolate, and estimate the severity of the fault(s), which has been addressed by many investigators over the last several decades.
2013 American Control Conference, 2013
In this paper, an efficient sensor fault detection and isolation (FDI) strategy is proposed based on multiple-model (MM) approach. The scheme is composed of hybrid kalman filters (HKF) by integrating a nonlinear gas turbine engine model that represents the operational engine model with a number of piecewise linear (PWL) models to estimate sensor outputs. The proposed FDI scheme is capable of detecting and isolating permanent sensor bias faults during the entire operational regime of the engine by interpolating the PWL models using a Bayesian approach. Another important aspect of our proposed FDI strategy is its effectiveness within the engine life cycle by periodically updating the model to the degraded health parameters, that one estimated by means of an off-line trend monitoring system that is based on post flight data. The simulation results demonstrate the effectiveness of our proposed online sensor fault diagnosis scheme as well as the robustness of our technique with respect to the engine health parameters degradations.
Model-based engine fault detection and isolation
2009 American Control Conference, 2009
To a large extent, tailpipe emissions are influenced by the accuracy and reliability of the intake manifold sensors and the predictive models used for cylinder charge estimation. In this paper, mathematical models of an internal combustion engine are employed to detect failures in the intake manifold. These can be associated with the upstream sensors such as the pressure and temperature sensors as well as systemic faults such as a leakage in the intake manifold. Any fault will adversely affect the proper operation of the air-fuel ratio control system and must be detected at an early stage. Through the use of dedicated observers, residual errors can be generated and thresholds established. Methods for the isolation of the detected faults are proposed and applied to a 5.7 L V8 engine model. Simulation results for the Federal Test Procedure (FTP) driving cycle indicate that fast and reliable detection and isolation of the faults is possible.
Probabilistic approach to the condition monitoring of aerospace engines
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2009
The provision of TotalCare® styled service offerings by original equipment manufacture (OEM) suppliers of high-integrity assets is intended to provide improved levels of system availability to the operator. A key element of such service offerings is the ability to minimize unplanned equipment downtime, and the utilization of advanced diagnostic and prognostic monitoring tools is a significant component in achieving this. Monitoring methods, founded on novelty detection technologies, are now a well-established condition monitoring technique. This approach is particularly appropriate for monitoring high-integrity plant where fault conditions arise with extremely low levels of probability. The approach described in this article is to establish empirically based models of normality that are guided by engineering knowledge and utilize key features normally used by expert engineers. However, rather than consider generic modelling approaches, it is proposed that application of models that ...