Reliable information storage in memories designed from unreliable components.
Reliable information storage in memories designed from unreliable components
Taylor, M. G.↗
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Reliable computation in computing systems designed from unreliable components.
Reliable computation in computing systems designed from unreliable components, considering two models for component malfunctions
Taylor, M. G.↗
Semi-Markov Unreliability-Range Evaluator
Reconfigurable, fault-tolerant systems modeled. Semi-Markov unreliability-range evaluator (SURE) computer program is software tool for analysis of reliability of reconfigurable, fault-tolerant systems. Based on new method for computing death-state probabilities of semi-Markov model. Computes accurate upper and lower bounds on probability of failure of system. Written in PASCAL.
Butler, Ricky W.↗
The cost of unreliability in the avionics suite of a launch vehicle
The main objective of the Advanced Launch System (ALS) program is to realize a substantial reduction in recurring launch costs over present launch systems. A methodology is presented for assessing the impact of the reliability of the avionics suite on the recurring launch cost of the ALS. The methodology is illustrated by focusing on two architectures. The first is a distillation of a typical architecture for this type of application. The second is a simplified implementation of the Advanced Information Processing System (AIPS) technology developed at the Charles Stark Draper Laboratory, Inc. Both architectures utilize redundancy of hardware to tolerate faults. the key contributors to the cost of unreliability are identified and modeled utilizing Markov modeling techniques for each of the two architectures. The results are presented along with the more traditional costs for these avionics suites.
Rosch, Gene↗
Semi-Markov Unreliability Range Evaluator
Semi-Markov Unreliability Range Evaluator, SURE, computer program is software tool for analysis of reconfigurable, fault-tolerant systems. Traditional reliability analyses based on aggregates of fault-handling and fault-occurrence models. SURE provides efficient means for calculating accurate upper and lower bounds for probabilities of death states for large class of semi-Markov mathematical models, and not merely those reduced to critical-pair architectures.
Butler, Ricky W.↗
Algorithms for Multiple Fault Diagnosis With Unreliable Tests
In this paper, we consider the problem of constructing optimal and near-optimal multiple fault diagnosis (MFD) in bipartite systems with unreliable (imperfect) tests. It is known that exact computation of conditional probabilities for multiple fault diagnosis is NP-hard. The novel feature of our diagnostic algorithms is the use of Lagrangian relaxation and subgradient optimization methods to provide: (1) near optimal solutions for the MFD problem, and (2) upper bounds for an optimal branch-and-bound algorithm. The proposed method is illustrated using several examples. Computational results indicate that: (1) our algorithm has superior computational performance to the existing algorithms (approximately three orders of magnitude improvement), (2) the near optimal algorithm generates the most likely candidates with a very high accuracy, and (3) our algorithm can find the most likely candidates in systems with as many as 1000 faults.
Shakeri, Mojdeh↗
Inferences based on unreliable reports
Human inferences based on partially reliable reports, studying likelihood ratio estimates and probabilistic relations in nature
Fryback, D. G.↗
The semi-Markov unreliability range evaluator program
The SURE program is a design/validation tool for ultrareliable computer system architectures. The system uses simple algebraic formulas to compute accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. The mathematical formulas used in the program were derived from a mathematical theorem proven by Allan White under contract to NASA Langley Research Center. This mathematical theorem is discussed along with the user interface to the SURE program.
Butler, R. W.↗
Semi-Markov Unreliability Range Evaluator (SURE)
Analysis tool for reconfigurable, fault-tolerant systems, SURE provides efficient way to calculate accurate upper and lower bounds for death state probabilities for large class of semi-Markov models. Calculated bounds close enough for use in reliability studies of ultrareliable computer systems. Written in PASCAL for interactive execution and runs on DEC VAX computer under VMS.
Butler, R. W.↗
Semi-Markov Unreliability Range Evaluator (SURE) And Pade Approximation With Scaling And Scaled Taylor Exponential Matrix (PAWS/STEM)
Together, programs form "reliability workbench", offering several theoretical approaches to design of redundant systems used in situations where failure catastrophic.
Butler, R. W.↗
Redundancy: How Many Unreliable Spares are Needed for High Reliability and Confidence on a Time Limited Mission?
This paper investigates the number of redundant units needed to achieve high reliability with high confidence. The approach applies to the case where the unit failure rate is too high for a single unit to provide the required reliability over the mission duration. To achieve high reliability, the design then uses N redundant units, one operating unit and N – 1 spares. If the unit failure rate is f, the mission length is L, and f * L is small (not the case assumed here), the unit failure probability over the mission duration is F1 = f * L << 1. In this case, the probability that all N units will fail is FN = F1N, and the needed N = LN(FN)/LN(F1). For the case of large f * L assumed here, F1 = f * L > 1, and F1 is the expected number of failures during the mission. The needed redundancy, N, to achieve the specified N unit reliability, FN, can be computed using the cumulative Poisson distribution with mean equal to F1. The number of spares, N - 1, is increased until the probability - that the total number of failures will be less than N -1 - achieves the required reliability. The confidence that this reliability can be achieved can be computed using the cumulative Poisson distribution or the chi-square distribution. Since the measured unit failure rate, f, has some uncertainty, the confidence that the rate is not lower than the actual failure rate and the required reliability is not overestimated is about 50%. Adding more redundant units increases the confidence that the required reliability, FN, will be achieved. For a fixed number of redundant units, the expected reliability and confidence can be traded off, since lower reliability goals have higher confidence in being achieved. Both the required reliability and confidence can be specified initially and the needed number of redundant units computed using the measured failure rate. The unit failure rate is determined by initial reliability growth testing to remove design errors and to better estimate the final constant failure rate. Reducing the failure rate and reducing its variance both reduce the number of redundant units needed for the required reliability and confidence. Since the total cost is the sum of the costs of the units and of the testing, there is an optimum test time that produces minimum cost.
Harry W. Jones↗
Redundancy: How Many Unreliable Spares are Needed for High Reliability and Confidence?
This paper investigates the number of redundant units needed to achieve high reliability with high confidence. The approach is developed for the case when the system failure rate is too high for a single unit to provide the required reliability over the mission duration. To achieve high reliability, N redundant units can be used, one operating unit and N – 1 spares. If the unit failure rate is f, the mission length is L, and f * L is small (not the case assumed here), the unit failure probability over the mission duration is F1 = f * L << 1. In this case, the probability that all N units will fail is Ffail = F1 N , and the needed redundancy N = LN(F)/LN(F1). For the case of large f * L assumed here, F1 = f * L > 1, and F1 is the expected number of failures during the mission. (When F1 = f * L << 1, F1 is the probability that a unit will fail during the mission. When F1 = f * L > 1, F1 is the expected number of failures during the mission.) The needed redundancy, N, to achieve the required N redundant unit reliability, FN, can be computed using the cumulative Poisson distribution with mean equal to F1. The number of spares, N - 1, is increased until the probability - that the total number of failures will be less than N -1 - is equal to the required reliability. The confidence that this reliability can be achieved can be computed using the cumulative Poisson distribution or the chi-square distribution. Since the measured unit failure rate, f, has some probabilistic uncertainty, the actual failure rate will be randomly higher or lower. This means that the reliability of the N redundant systems will be overestimated about half the time. Adding more redundant units increases the confidence that the required reliability will be achieved. For a fixed number of redundant units, the expected reliability and confidence can be traded off, since lower reliability goals will be achieved with higher confidence. Both the desired reliability and confidence can be specified as initial requirements and the needed number of redundant units estimated using the measured failure rate.
Redundancy↗
Redundancy: How Many Unreliable Spares are Needed for High Reliability and Confidence?
Explore the source record for details and available documents.
Redundancy↗
APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments
Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.
Autonomous systems↗
APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments
Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.
Autonomous systems↗
DHARMA - Discriminant hyperplane abstracting residuals minimization algorithm for separating clusters with fuzzy boundaries
Learning of discriminant hyperplanes in imperfectly supervised or unsupervised training sample sets with unreliably labeled samples along the fuzzy joint boundaries between sample clusters is discussed, with the discriminant hyperplane designed to be a least-squares fit to the unreliably labeled data points. (Samples along the fuzzy boundary jump back and forth from one cluster to the other in recursive cluster stabilization and are considered unreliably labeled.) Minimization of the distances of these unreliably labeled samples from the hyperplanes does not sacrifice the ability to discriminate between classes represented by reliably labeled subsets of samples. An equivalent unconstrained linear inequality problem is formulated and algorithms for its solution are indicated. Landsat earth sensing data were used in confirming the validity and computational feasibility of the approach, which should be useful in deriving discriminant hyperplanes separating clusters with fuzzy boundaries, given supervised training sample sets with unreliably labeled boundary samples.
Dasarathy, B. V.↗
Calculating parts factors for redundant systems
Method that is easily programmed simplifies calculation of parts factor. Individual module unreliabilities are computed as function of number of service intervals and service interval length. At each service interval, unreliability is sum of unreliabilities of replaced and original modules. It must be calculated for each module to obtain parts factor.
Derocher, W. L., Jr.↗
Cost benefits of advanced software: A review of methodology used at Kennedy Space Center
To assist rational investments in advanced software, a formal, explicit, and multi-perspective cost-benefit analysis methodology is proposed. The methodology can be implemented through a six-stage process which is described and explained. The current practice of cost-benefit analysis at KSC is reviewed in the light of this methodology. The review finds that there is a vicious circle operating. Unsound methods lead to unreliable cost-benefit estimates. Unreliable estimates convince management that cost-benefit studies should not be taken seriously. Then, given external demands for cost-benefit estimates, management encourages software enginees to somehow come up with the numbers for their projects. Lacking the expertise needed to do a proper study, courageous software engineers with vested interests use ad hoc and unsound methods to generate some estimates. In turn, these estimates are unreliable, and the cycle continues. The proposed methodology should help KSC to break out of this cycle.
Joglekar, Prafulla N.↗