Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

MATERIALS AND STRUCTURES PROGNOSIS FOR GAS TURBINE ENGINES

ABSTRACT Gas turbine engine diagnostic systems often utilize data trending and anomaly detection to provide a measure of system health. These systems provide significant benefits for trending shifts in engine performance and diagnosing system degradation that requires some maintenance action. However, this approach may be limited in the ability to uniquely identify damage for select components and failure modes. Advanced prognostic systems are being developed to work symbiotically with state of the art diagnostic techniques in use today; these advanced systems use advanced material and component damage evolution modelling linked with system-level structural analyses to intelligently guide the health management system to search for specific signatures that would be expected from key changes in component and system health Material damage models, advanced component models, and novel system-level structural analyses are being used to generate newly defined "structural transfer functions" (STFs) that provide a link between sensed parameters and the remaining capability of specific components, and the system. The characteristic damage signatures vary by component type and failure mode, and hence the specific STF approach varies among component types. An initial STF approach was developed and demonstrated for a specific component and damage typ

CLASSIFICATION OF TEXTURED SURFACES BASED ON REFLECTION DATA (IMAGE-PROCESSING, COMPUTER VISION)

Recognition of surfaces is very important in computer vision. Color and texture of a surface are two key characteristics that influence the surface recognition process. Light reflected from a surface contains information about these two characteristics. The color of a surface determines how a surface reflects light of a specific wavelength, and the surface texture determines the amount of diffuse and specular reflection taking place from the surface.^ A statistical approach has been developed for classification of surfaces based on the spatial intensity distribution of light reflected from the surfaces. In this approach, a surface is modelled as a collection of mirror-like micro-facets oriented randomly with respect to each other. The random orientations of the micro-facets give rise to spatial intensity distribution of the reflected light. The reflected light field is characterized by a probability density function, which is used for deriving first and second order features. The first order features are the mean and variance of the reflected intensities, while the second order features are based on the spatial correlation between the reflected intensities. A correlation matrix is formulated based on the co-occurrence of two given intensities separated by a given angular distance. Two classification schemes based on maximum-likelihood and nearest-neighbor decision rules are implemented on the feature sets.^ Experimental results for the classification schemes are presented for a variety of sample surfaces. These include paper, cloth, felt, sandpaper, cork, crumpled and smooth aluminum foils, etc. The success rate of 80-100% has been achieved for the two classification schemes.^ In addition to these statistical features, other properties of textures such as gloss, contrast, roughness, etc., have also been measured from the reflected intensities. Parameters such as micro-facet slope function, micro-facet orientation function, etc., have been measured and used for predicting the statistics of the perceived texture. Techniques have been developed to reduce the computational burden associated with this reflection data based surface classification scheme in order to make it suitable for on-line operation.