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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.

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.

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. Nomenclature Variables 'This work was supported by the U.S. Army under grant number NAG3-1198. 14. SUBJECT TERMS 15. NUMBER OF PAGES Real-time simulator; Helicopter engines; Mathematical models 16. PRICE CODE A03 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF THIS PAGE OF ABSTRACT Unclassified Unclassified Unclassified NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Sid Z39-18 298-102

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The paper discusses how the Electrical Submersible Pumps (ESP) mechanical and electrical malfunctions are reflected in the dynamic current spectrum using Motor Current Signature Analysis (MCSA). The paper further shows the real case studies, analyses and findings on ESP. Since MCSA does not require any sensor installation on the ESP itself and the measurement is carried out remotely in the ESP motor control unit, it is especially attractive for inaccessible driven equipment and well suited for the monitoring of Electrical Submersible Pumps.

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