Project ''diode reliability prediction technique''
Mathematical models for predicting reliability of semiconductor diodes
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Mathematical models for predicting reliability of semiconductor diodes
CARE3MENU generates input file for CARE III program. Used to predict reliabilities of complex, redundant, fault-tolerant systems, including digital computers, aircraft, and nuclear and chemical control systems. CARE III input file often becomes complicated and not easily formatted with text editor. Provides easy interactive method of creating input file by automatically formatting set of user-supplied inputs for CARE III system. CARE3MENU provides detailed online help for most of its screen formats.
Abstract not provided.
This report documents the final reliability prediction performed on the Earth Observing System/Advanced Microwave Sounding Unit-A (EOS/AMSU-A). The A1 Module contains Channels 3 through 15, and is referred to herein as 'EOS/AMSU-A1'. The A2 Module contains Channels 1 and 2, and is referred herein as 'EOS/AMSU-A2'. The 'specified' figures were obtained from Aerojet Reports 8897-1 and 9116-1. The predicted reliability figure for the EOS/AMSU-A1 meets the specified value and provides a Mean Time Between Failures (MTBF) of 74,390 hours. The predicted reliability figure for the EOS/AMSU-A2 meets the specified value and provides a MTBF of 193,110 hours.
Different software reliability models can produce very different answers when called upon to predict future reliability in a reliability growth context. Users need to know which, if any, of the competing predictions are trustworthy. Some techniques are presented which form the basis of a partial solution to this problem. Rather than attempting to decide which model is generally best, the approach adopted here allows a user to decide upon the most appropriate model for each application.
Reliability predictions showing need of bench- mark with respect to component part failure rates
Reliability prediction, modeling and analysis activities in Apollo program
Computer program for system reliability prediction, using probability tree approach and block probabilities
This report documents the reliability prediction performed on the Meteorological Satellites (METSAT) and the Earth Observing System (EOS) Advanced Microwave Sounding Unit-A (AMSU-A) instruments.
A review and a critical evaluation of a representative class of state-of-the-art models for ultrahigh reliability prediction is presented. This evaluation naturally leads to a new model for ultrahigh reliability prediction now under development. The new model combines the flexibility and accuracy of simulation with the speed of analytic models.
Three theorems which show that the reliability of a popular class of systems can be computed using small and simple models are presented. This class consists of systems that are assemblages of subsystems where each subsystem is a majority-voting threeplex plus spares or majority-voting fourplex plus spares. The theorems are error bounds for model reduction and simplification. The error bounds are given in terms of readily available system parameters. The three theorems have been applied to a system that has been used as an example that generates extremely large reliability models; the system considered is one version of AIPS (Advanced Information Processing System) for IAPSA (Integrated Airframe Propulsion System Architecture).
New fundamental technique of reliability prediction for semiconductor diodes based on realistic mathematical models can be applied to component failure rate prediction including mechanical degradation, electrical degradation, environmental stress factors, and electrical load stress factors.
A new method of reliability prediction for complex systems is defined. Calculation of both upper and lower bounds are involved, and a procedure for combining the two to yield an approximately true prediction value is presented. Both mission success and crew safety predictions can be calculated, and success probabilities can be obtained for individual mission phases or subsystems. Primary consideration is given to evaluating cases involving zero or one failure per subsystem, and the results of these evaluations are then used for analyzing multiple failure cases. Extensive development is provided for the overall mission success and crew safety equations for both the upper and lower bounds.
Robustness of reliability predictions for series systems with identical components, assuming exponential failure distribution
In the proposed modeling approach, when any of the essential key factors are not known initially, they can be approximated in various ways with a known impact on the accuracy of the final predictions. For example, on any program where reliability predictions are started at interim states of project completion, a-priori approximate estimates of the key factors are established for making preliminary predictions. Later these are refined for greater accuracy as subsequent program information of a more definitive nature becomes available. Specific steps to develop, validate and verify these new models are described.
The robustness of reliability predictions based on the exponential failure law is investigated under possible deviations within the Weibull family of failure distributions. Regions of robustness are provided for series systems of N identical components, N = 1(1)15; i.e., regions in the space of the Weibull shape parameter, within which one may safely use the exponential prediction procedure and have no more than a prespecified error.
Microstructural variabilities are among the predominant sources of uncertainty in structural performance and reliability. We seek to develop efficient algorithms for multiscale calcu- lations for polycrystalline alloys such as aluminum alloy 6061-T6 in environments where ductile fracture is the dominant failure mode. Our approach employs concurrent multiscale methods, but does not focus on their development. They are a necessary but not sufficient ingredient to multiscale reliability predictions. We have focused on how to efficiently use concurrent models for forward propagation because practical applications cannot include fine-scale details throughout the problem domain due to exorbitant computational demand. Our approach begins with a low-fidelity prediction at the engineering scale that is sub- sequently refined with multiscale simulation. The results presented in this report focus on plasticity and damage at the meso-scale, efforts to expedite Monte Carlo simulation with mi- crostructural considerations, modeling aspects regarding geometric representation of grains and second-phase particles, and contrasting algorithms for scale coupling.