Ahmet Duyar

Fault detection and diagnosis in propulsion systems - A fault parameter estimation approach

The paper presents the development of a fault detection and diagnosis (FDD) system with applications to the Space Shuttle main engine. The FDD utilizes a model-based method with real-time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Fault diagnosis for the Space Shuttle main engine

A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Fault diagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.

A Simplified Dynamic Model of the T700 Turboshaft Engine

Abstract : A simplified open-loop dynamic model of the T700 turboshaft engine, valid within the normal operating range of the engine, is developed. This model is obtained by linking linear state space models obtained at different engine operating points. Each linear model is developed from a detailed nonlinear engine simulation using a multivariable system identification and realization method. The simplified model may be used with a model-based real time diagnostic scheme for fault detection and diagnostics, as well as for open loop engine dynamics studies and closed loop control analysis utilizing a user generated control law.

Space shuttle main engine model identification

System identification techniques are used to represent the dynamic behavior of the SSME (space shuttle main engine). The comparison of the responses of a nonlinear simulation with the responses of an identified model indicates very good agreement. The identified model can be used for control design purposes. The identified model does not include valve linkage backlash and valve stiction nonlinearities. These nonlinearities should be added to the identified model, and the validity of the model should be checked by comparing it with the full nonlinear simulation. The identified model is valid for a limited response region at about the 100% power level operating condition.<<ETX>>

Modeling space shuttle main engine using feed-forward neural networks

This paper presents the modeling of the Space Shuttle Main Engine (SSME) using a feed-forward neural network. The input and output data for modeling are obtained from a non-linear performance simulation developed by Rockwell International. The SSME is modeled as a system with two inputs and four outputs. The back-propagation algorithm is used to train the neural network by minimizing the squares of the residuals. The inputs to the network are the delayed values of the selected inputs and outputs of the non-linear simulation. The results obtained from the neural network model are compared with the results obtained from the non-linear simulation. It is shown that a single neural network can be used to model the dynamics of the space shuttle main engine. This neural network model can be used for control design purposes as well as for model-based fault detection studies.

A failure diagnosis system based on a neural network classifier for the Space Shuttle main engine

A model-based failure diagnosis system based on a neural network classifier for the Space Shuttle main engine (SSME) is described. It relies on the accurate and reliable identification of the parameters of the highly nonlinear and very complex engine. The system may be used to monitor the life cycle of engine components and for the early detection, isolation, and diagnosis of engine failures. Thus the proposed system will be one part of a larger engine health monitoring system. The design approach is presented in some detail, along with the results for a failed valve. The preliminary results verify that the developed parameter identification technique, together with a neural network classifier, can be used for the detection and diagnosis of valve failure.<<ETX>>

Parametrization of multivariable systems using output injections: alpha canonical forms

Abstract In this paper the parametrization of multi-output systems is considered. The method developed is based on the notion of output injections. A similar method has also been used in connection with the identification of a special class of systems. There, the resulting canonical form is called the α-canonical form. Here, using the same terminology, we address the following issues on the α-canonical form. First, we show that the α-canonical forms can be used to parametrize a more general class of systems, namely all minimal (reachable and observable) systems. This is achieved by the use of isomorphisms of the output space. Then, it is proven that the output injections used in constructing the α-canonical forms need to have a special structure in order to guarantee the uniqueness of the parametrization. This result also reveals the key role played by dead-beat observers in constructing the α-canonical forms. Finally, the connections between the α-canonical forms and input-output relations are investigated.

A Distributed Fault-Detection and Diagnosis System Using On-Line Parameter Estimation

Abstract This paper describes a model-based fault-detection and diagnosis system based on a distributed system identification approach. The diagnostic system consists of a two level process including parallel hypothesis testing modules and a fault mode identification and estimation module. The proposed system is part of a distributed diagnostic system for use in an intelligent control system. The proposed approach utilizes a piecewise linear model to predict the system performance. The deviation between predicted and actual performance is used to identify the associated fault mode. Each hypothesis testing module is associated with a particular class of fault modes and can be viewed as a condition monitor in a distributed diagnostic system hierarchy. The results of the hypothesis modules are processed by the fault-detection and estimation module. Using the results of the on-line diagnosis, the intelligent control system will be able to accommodate the fault modes, reduce maintenance cost, and increase system availability.

Model-Based Fault Diagnosis for Turboshaft Engines

Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

A Simplified Dynamic Model of the Space Shuttle Main Engine

This paper presents a simplified model of the space shuttle main engine (SSME) dynamics valid within the range of operation of the engine. This model is obtained by linking the linearized point models obtained at twenty five differenc operating points of the SSME. The simplified model was developed for use with a model-based diagnostic scheme for failure detection and diagnostics studies, as well as control design purposes.