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Three candidate fault tolerant hypercube architectures are modeled, their reliability analyses are compared, and the resulting implications of these methods of incorporating fault tolerance into hypercube multiprocessors are discussed. In the course of performing the reliability analyses, the use of HARP and fault trees in modeling sequence dependent system behaviors is demonstrated.
Simulations serve as important tools throughout the design and operation of engineering systems. In the context of sys-tems health management, simulations serve many uses. For one, the underlying physical models can be used by model-based health management tools to develop diagnostic and prognostic models. These simulations should incorporate both nominal and faulty behavior with the ability to inject various faults into the system. Such simulations can there-fore be used for operator training, for both nominal and faulty situations, as well as for developing and prototyping health management algorithms. In this paper, we describe a methodology for building such simulations. We discuss the design decisions and tools used to build a simulation of a cryogenic fluid test bed, and how it serves as a core technology for systems health management development and maturation.
In this paper we present a novel lightweight approach to validate compilers for synchronous languages. Instead of verifying a compiler for all input programs or providing a fixed suite of regression tests, we extend the compiler to generate a test-suite with high behavioral coverage and geared towards discovery of faults for every compiled artifact. We have implemented and evaluated our approach using a compiler from Lustre to C.
We present a baseline approach to Functional Autonomyfor the purpose of conducting excavation and samplingbehaviors in a proposed Europa Lander mission. Aspects ofthe problems of site selection, excavation progress tracking,and fault identification are related; with particular emphasison parameters peculiar to an icy moon environment. Firstpass approaches to addressing these challenges are presentedin isolation, while motivating the current development goal ofproducing a general Functional Autonomy architecture thatallows state estimation, fault diagnosis, isolation, & recovery(FDIR), and adaptive behaviors to be formulated in concert.
The goal of this project remains to be the achievement of a better understanding of the regional and local deformation and crustal straining processes in western North America, particularly the effect of the San Andreas and nearby faults on the spatial and temporal crustal deformation behavior. Construction of theoretical models based on the mechanics of coupled elastic plate/viscoelastic foundation and large scale crack mechanics provide a rational basis for the interpretation of seismic and aseismic anomalies and expedite efforts in forecasting the stability of plate boundary deformations. In the present period, special focus is placed on the 3-D effect of irregular fault locked patches on the ground measured deformation fields. Specifically, use is made of a newly developed 3-D boundary element program to analyze the fault slip and vertical ground motion in the Parkfield area on the San Andreas.
In this paper we investigate the effectiveness of a pattern classifying fault detection system that is designed to cope with the variability of fault signatures inherent in helicopter gearboxes. For detection, the measurements are monitored on-line and flagged upon the detection of abnormalities, so that they can be attributed to a faulty or normal case. As such, the detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and influence matrix are tuned during a training session so as to minimize the error in detection. To demonstrate the effectiveness of this detection system, it was applied to vibration measurements collected from a helicopter gearbox during normal operation and at various fault instances. The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set.
Fault protection as a discipline involves a collection of flight software logic and operational processes for detecting unacceptable anomalous behavior, responding prior to reaching criticality, restricting the propagation of a failure beyond a fault containment region, and recovering the vehicle back to full or degraded functionality if possible. The System Fault Protection (SFP) design for the SMAP Earth orbiter was put to the test during its 90-day vehicle commissioning activities. During this time, the SFP software autonomously protected the vehicle from multiple faults to critical hardware, and the operations team successfully returned the observatory to its science state. The SFP also performed well in the presence of anomalous behavior below true safety limits by not taking unnecessary response actions, instead allowing the operations team time to monitor the behavior. Certain aspects of the SFP design were modified during operations via both parameter updates and a full flight software update in order to better match the vehicle behavior in the flight environment. An evaluation of the SMAP SFP performance during vehicle Commissioning will be provided in this paper, as well as a set of lessons learned largely focused on visibility, SFP mutability in operations, responses to peripheral device faults, and Safe Mode recovery and design. By capturing some of the knowledge gained during SMAP Commissioning, it is intended that this paper provide guidance for making future System Fault Protection designs more robust and supportive of operations.
Software reliability research is briefly described. General research topics are reliability growth models, quality of software reliability prediction, the complete monotonicity property of reliability growth, conceptual modelling of software failure behavior, assurance of ultrahigh reliability, and analysis techniques for fault-tolerant systems.
Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.
Simple linear rheologies were used with elastic forces driving the main events and viscoelastic forces being important for aftershock and creep occurrence. Friction and its dependence on velocity, stress, and displacement also plays a key role in determining how, when, and where fault motion occurs. The discussion of the qualitative behavior of the simulators focuses on the manner in which energy was stored in the system and released by the unstable and stable sliding processes. The numerical results emphasize the statistics of earthquake occurrence and the correlations among source parameters.
This paper reviews the simulation of earthquake occurrence by numerical and laboratory mechanical block models. Simple linear rheological elements are used with elastic forces driving the main events and viscoelastic forces being important for aftershock and creep occurrence. Friction and its dependence on velocity, stress, and displacement also play a key role in determining how, when, and where fault motion occurs. The discussion of the qualitative behavior of the simulators focuses on the manner in which energy is stored in the system and released by the unstable and stable sliding processes. The numerical results emphasize the statistics of earthquake occurrence and the correlations among source parameters.
The details are presented of an onboard digital computer algorithm designed to reliably detect and isolate the first failure in a duplex set of flight control sensors aboard the NASA F-8 digital fly-by-wire aircraft. The algorithm's successful flight test program is summarized, and specific examples are presented of algorithm behavior in response to software-induced signal faults, both with and without aircraft parameter modeling errors.
A study of large intraplate earthquakes with well determined source parameters shows that these earthquakes obey a scaling law similar to large interplate earthquakes, in which M sub o varies as L sup 2 or u = alpha L where L is rupture length and u is slip. In contrast to interplate earthquakes, for which alpha approximately equals 1 x .00001, for the intraplate events alpha approximately equals 6 x .0001, which implies that these earthquakes have stress-drops about 6 times higher than interplate events. This result is independent of focal mechanism type. This implies that intraplate faults have a higher frictional strength than plate boundaries, and hence, that faults are velocity or slip weakening in their behavior. This factor may be important in producing the concentrated deformation that creates and maintains plate boundaries.
The goal is to achieve a better understanding of the regional and local deformation and crustal straining processes in western North America, particularly the effects of the San Andreas and nearby faults on the spatial and temporal crustal deformation behavior. Construction of theoretical models based on the mechanics of coupled elastic plate, viscoelastic foundation and large scale crack mechanics provide a rational basis for the interpretation of seismic and aseismic anomalies and expedite efforts in forecasting the stability of plate boundary deformation. Special focus is placed on the three dimensional time dependent surface deformation due to localized slippage in a elastic layer coupled to a visco-elastic substrate. The numerical analysis is based on a 3-D boundary element technique. Extension to visco-elastic coupling demands the derivation of 3-D time dependent Green's function. This method was applied to analyze the viscoelastic surface displacements due to a dislocated embedded patch. Surface uplift as a function of time and position are obtained. Comparisons between surface uplift for long and short dislocated patches are made.
Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.
Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.
Dependability evaluation involves the study of failures and errors. The destructive nature of a crash and long error latency make it difficult to identify the causes of failures in the operational environment. It is particularly hard to recreate a failure scenario for a large, complex system. To identify and understand potential failures, we use an experiment-based approach for studying the dependability of a system. Such an approach is applied not only during the conception and design phases, but also during the prototype and operational phases. To take an experiment-based approach, we must first understand a system's architecture, structure, and behavior. Specifically, we need to know its tolerance for faults and failures, including its built-in detection and recovery mechanisms, and we need specific instruments and tools to inject faults, create failures or errors, and monitor their effects.
An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.