Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters

A real time neural net estimator of fatigue life

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines

Apparatus for applying simulator g-forces to an arm of an aircraft simulator pilot

A device to be used with an aircraft simulator to apply positive and negative g forces to the pilot's arm is described. An arm harness fits around the arm which the pilot uses to operate the throttle. The device allows the harness to track intentional arm movements without exerting any restraining forces, and at the same time, applies g forces to to the pilots arm which are recorded by the aircraft simulator computer

Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem

The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design

PEM-West trajectory climatology and photochemical model sensitivity study prepared using retrospective meteorological data

Trajectory and photochemical model calculations based on retrospective meteorological data for the operations areas of the NASA Pacific Exploratory Mission (PEM)-West mission are summarized. The trajectory climatology discussed here is intended to provide guidance for flight planning and initial data interpretation during the field phase of the expedition by indicating the most probable path air parcels are likely to take to reach various points in the area. The photochemical model calculations which are discussed indicate the sensitivity of the chemical environment to various initial chemical concentrations and to conditions along the trajectory. In the post-expedition analysis these calculations will be used to provide a climatological context for the meteorological conditions which are encountered in the field

A distributed fault-detection and diagnosis system using on-line parameter estimation

The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes

Real-time diagnostics for a reusable rocket engine

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2

Diffusion-based method for producing density equalizing maps

Map makers have long searched for a way to construct cartograms -- maps in which the sizes of geographic regions such as countries or provinces appear in proportion to their population or some other analogous property. Such maps are invaluable for the representation of census results, election returns, disease incidence, and many other kinds of human data. Unfortunately, in order to scale regions and still have them fit together, one is normally forced to distort the regions' shapes, potentially resulting in maps that are difficult to read. Many methods for making cartograms have been proposed, some of them extremely complex, but all suffer either from this lack of readability or from other pathologies, like overlapping regions or strong dependence on the choice of coordinate axes. Here we present a new technique based on ideas borrowed from elementary physics that suffers none of these drawbacks. Our method is conceptually simple and produces useful, elegant, and easily readable maps. We illustrate the method with applications to the results of the 2000 US presidential election, lung cancer cases in the State of New York, and the geographical distribution of stories appearing in the news.Comment: 12 pages, 3 figure

Evaluating duration of response to treatment in systemic lupus erythematosus clinical trials.

ObjectiveTo evaluate response duration and identify predictors of transitioning into and out of the response state in patients with SLE receiving standard of care (SoC) in 52-week clinical trials.MethodsA multistate model (MSM) allowing for bidirectional transitions between response and non-response states was fit to data on 759 patients with SLE with active disease randomised to SoC. The probability of being in response at 52 weeks, average duration of response (sojourn time) and mean total time in response for SLE Responder Index (SRI-4, SRI-5, SRI-6) and BILAG-based Composite Lupus Assessment (BICLA) were estimated. Predictors of attainment and loss of SRI-5 response were also assessed.ResultsThe MSM estimated probability of being in response at 52 weeks ranged from 42% (SRI-6) to 61% (SRI-4). Mean duration of response ranged from 20.4 weeks (BICLA) to 31.5 weeks (SRI-4). Mean total time in response was 16.4-24.8 weeks. Baseline characteristics predictive of shorter SRI-5 response duration were African descent (p=0.005), longer history of disease (p=0.03), higher anti-dsDNA antibody titres (p=0.039), lower lymphocyte count (p=0.008) and lower haemoglobin (p=0.006). Younger age (p<0.001) and higher protein/creatinine ratio (p<0.001) were associated with higher likelihood of achieving SRI-5 but also shorter response duration.ConclusionFactors associated with disease severity were more predictive of shorter response duration than of 52-week response status. Analysing landmark response rates and response duration using MSM may be a more powerful way to distinguish effective investigational treatments from background SoC, although this remains to be evaluated in future trials

Environmental influences on hurricane intensification

December, 1985.Includes bibliographical references (pages 139-149).Though qualitatively similar in structure, different hurricanes can attain different peak intensities during their lifetimes. Forecasters and empiricists relate the intensity to the sea surface temperature and the "effectiveness" of the upper tropospheric outflow, but offer no clear explanation of how the latter operates. Numerical modelers usually ignore the surrounding flow and emphasize interaction between the convective and vortex scales exclusively. This paper examines more closely the observed upper-tropospheric environmental flow differences between hurricanes which intensify and those which fail to do so, and combines them with previously published empirical and modeling results into a general conceptual model of environmental influences on hurricane intensification. Upper tropospheric wind observations (from satellite cloud tracking. aircraft reports, and rawinsondes) are composited for 28 hurricanes according to intensity tendency. A rotated coordinate system based on the outflow jet location is used so that the asymmetric flow structure is preserved. Little difference is observed in total outflow on the synoptic scale. However, intensifying hurricanes have a less constricted outflow with evidence or lateral connections with the surrounding flow. The asymmetric flow consists of a wave thought to be associated with barotropic instability or the anticyclonic flow above the hurricane and the juxtaposition of surrounding flow features. A quasi-equilibrium balance between hurricane convection and the upper tropospheric environment is proposed. The moist-neutral stratification of the vortex core is a balance between the convection which acts to increase stability and the outflow which acts to reduce it. Reduce the outflow layer cooling and the core stabilizes convective buoyancy is reduced, and a new balance with less vigorous convection is established. If vertically sheared, the environmental flow can also regulate intensity by inducing asymmetric convective structure. Vortex-convection feedbacks are considered to be important mainly in the stages of tropical cyclone development prior to eye formation, which is seen as the first indication that the stabilization process is occurring. Several observational and numerical tests for this conceptual model are then proposed.Sponsored by NSF/NOAA, grant no. ATM-8419116, National Science Foundation