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System effectiveness models: an annotated bibliography
World Abstracts on Microelectronics and Reliability
279
Human error considerations in determining the optimum test interval for periodically inspected standby systems. T. P. MCWILLIAMS and H. F. MARTZ. IEEE Trans. Reliab. R-29 {4). 305 {October 1980). This paper incorporates the effects of two types of human error in a model for determining the optimal time between inspections for a safety system. The possibility that a bad safety system is undetected upon inspection (Type B human error), as well as the possibility that a good safety system is inadvertently left in a bad state after the inspection (Type A human error), are considered. We develop a Markov model for the steady-state availability of the safety system which is then used to determine the optimum time between inspections which either maximizes the availability or minimizes the combined inspection and unavailability costs. The safety system failure {hazard) rate need not be constant. The optimum time between inspections increases as the probability of a Type A error increases and a Type B error decreases. The optimum availability decreases and the optimum total cost increases as the error probabilities increase.
effectiveness models dealing with military and space systems have been developed by the US Air Force, Army, and Navy. Each model has different attributes in its definition of system effectiveness. This survey classifies these attributes and definitions and presents their relationship to system effectiveness. Attributes included are reliability, availability, operational readiness, repairability, maintainability, serviceability, design adequacy, capability, dependability, human performance, and environmental effects. The system effectiveness models and their computer codes are also classified and reviewed.
Comparison of Monte Carlo techniques for obtaining systemreliability confidence limits. ALBERT H. MOORE, H. LEON HARTER and ROBERTC. SNEAD. IEEE Trans. Reliab. R-29 (4), 327 {October 1980). Digital computer techniques are developed using (a) asymptotic distributions of maximum likelihood estimators, and (b) a Monte Carlo technique, to obtain approximate system reliability s-confidence limits from component test data. 2-Parameter Weibull, gamma, and logistic distributions are used to model the component failures. The components can be arranged in any system configuration : series, parallel, bridge, etc., as long as one can write the equation for system reliability in terms of component reliability. Hypothetical networks of 3, 5, and 25 components are analyzed as examples. Univariate and bivariate asymptotic techniques are compared with a double Monte Carlo method. The bivariate asymptotic technique is shown to be fast and accurate. It can guide decisions during the research and development cycle prior to complete system testing and can be used to supplement system failure data.
Protection system reliability modeling: unreadiness probability and mean duration of undetected faults. C. SINGH and A. D. PATTON. IEEE Trans. Reliab. R-29 (4), 339 (October 1980). A reliability model of a system and its associated protection system is described. The protection system detects the presence of faults and isolates the faulted equipment so as to prevent damage and minimize the effect of the faulted equipment on the operation of the rest of the system. If the protection system does not respond and the faulted component is not disconnected, a backup protection system comes into operation. The backup operation isolates a larger segment and disturbs the system more than if the designated protection system had responded appropriately. An example is the protective relay systems for electric power transmission and distribution systems. A measure of system unreadiness is defined and suitable expressions are derived for this index and the mean duration of undetected faults. The unreadiness probability is sensitive not only to the failure rate of the protection system but also to the failure rate of the system, the pdf of interval between inspections and its mean value. The unreadiness probability estimated from a given system cannot be used for another system having a similar protection system. The protection system failure rate can, however, be used t~ obtain the unreadiness probability.
Operational availability of a multi-component sequential system with imperfect switching and opportunistic repair under preemptive resume repair policy. ANIL KUMAR. The QR JI (India) p. 81 (May 1980). In this paper, various aspects of reliability of a complex system consisting of three subsystems, in series, with a single repair facility, have been evaluated. One is made up of two identically redundant units whereas, the others are different, independent, repairable units. The repairs are carried out under priority basis. Concepts of shelf-life, opportunistic repair and imperfect sensor/switch have been introduced. "Inclusion of supplementary variable" and Laplace transforms techniques have been used to evaluate the operational availability of the system. A new bad data detection and identification algorithm. R. DORAISWAMIand J. L. R. PEREIRA. IEEE Trans. Reliab. R-29 (4). 333 {October 1980). A fast and reliable algorithm for detecting and identifying bad data in active-power measurements is proposed. The states are chosen to be the bus-voltage magnitudes and bus-voltage angle-differences. The bus-voltage angle-differences satisfy a set of loop equations derived from the topology of the network and a set of power-flow equations at nodes with no generations and loads. The algorithm is based on inspecting a similar set of equations. The proposed algorithm is tested on a realistic example and the results are compared with those of the conventional bad-data elimination scheme. System effectiveness models: an annotated bibliography. F. A. TILLMAN,C. L. HWANG and WAY KuO. IEEE Trans. Reliab. R-29 (4), 295 {October 1980). A state-of-the-art survey on system effectiveness models is presented. A variety of system
Availability analysis of a fault-tolerant computer system. MARIO DAL GIN. IEEE Trans, Reliab. R-29 (3), 265 (August 1980). Queueing network theory is applied in order to determine the availability of a fault-tolerant multiprocessor computer system. Mean repair times and utilizations of system units are computed. Subsystem availabilities are obtained by the decomposition technique of queueing network theory.
Hazard modification techniques for repair and multiple operating conditions. W. J. KOLARIK and K. E. CASE. IEEE Trans. Reliab. R-29 {4), 324 {October 1980). Two modeling techniques for simulating availability are developed. A technique is developed for introducing multiple operating conditions into simulation models. Another technique is developed for incorporating an operating-time adjustment for repair in availability simulations. A general operatingtime adjustment-technique is developed and then two special cases of this technique are discussed. The two modeling techniques developed in this paper allow the availability engineer to obtain more detail in availability studies. These techniques were used in the Farm Machinery Availability Cost Simulation Model. A review of standby redundant systems. ASHOK KUMAR and MANJU AGARWAL.IEEE Trans. Reliab. R-29 (4), 290 (October 1980). This article critically reviews the existing literature on standby redundant systems. Concepts related to standby systems have been defined. Parameters in which system designers and engineers may be interested have been discussed. Some of these parameters are mean-time-to-system failure, s-expected number of visits to a state, steady-state availability, and s-expected profit rate of the system. The types of systems discussed in literature and various methods employed by different workers in the analysis have been
279
Human error considerations in determining the optimum test interval for periodically inspected standby systems. T. P. MCWILLIAMS and H. F. MARTZ. IEEE Trans. Reliab. R-29 {4). 305 {October 1980). This paper incorporates the effects of two types of human error in a model for determining the optimal time between inspections for a safety system. The possibility that a bad safety system is undetected upon inspection (Type B human error), as well as the possibility that a good safety system is inadvertently left in a bad state after the inspection (Type A human error), are considered. We develop a Markov model for the steady-state availability of the safety system which is then used to determine the optimum time between inspections which either maximizes the availability or minimizes the combined inspection and unavailability costs. The safety system failure {hazard) rate need not be constant. The optimum time between inspections increases as the probability of a Type A error increases and a Type B error decreases. The optimum availability decreases and the optimum total cost increases as the error probabilities increase.
effectiveness models dealing with military and space systems have been developed by the US Air Force, Army, and Navy. Each model has different attributes in its definition of system effectiveness. This survey classifies these attributes and definitions and presents their relationship to system effectiveness. Attributes included are reliability, availability, operational readiness, repairability, maintainability, serviceability, design adequacy, capability, dependability, human performance, and environmental effects. The system effectiveness models and their computer codes are also classified and reviewed.
Comparison of Monte Carlo techniques for obtaining systemreliability confidence limits. ALBERT H. MOORE, H. LEON HARTER and ROBERTC. SNEAD. IEEE Trans. Reliab. R-29 (4), 327 {October 1980). Digital computer techniques are developed using (a) asymptotic distributions of maximum likelihood estimators, and (b) a Monte Carlo technique, to obtain approximate system reliability s-confidence limits from component test data. 2-Parameter Weibull, gamma, and logistic distributions are used to model the component failures. The components can be arranged in any system configuration : series, parallel, bridge, etc., as long as one can write the equation for system reliability in terms of component reliability. Hypothetical networks of 3, 5, and 25 components are analyzed as examples. Univariate and bivariate asymptotic techniques are compared with a double Monte Carlo method. The bivariate asymptotic technique is shown to be fast and accurate. It can guide decisions during the research and development cycle prior to complete system testing and can be used to supplement system failure data.
Protection system reliability modeling: unreadiness probability and mean duration of undetected faults. C. SINGH and A. D. PATTON. IEEE Trans. Reliab. R-29 (4), 339 (October 1980). A reliability model of a system and its associated protection system is described. The protection system detects the presence of faults and isolates the faulted equipment so as to prevent damage and minimize the effect of the faulted equipment on the operation of the rest of the system. If the protection system does not respond and the faulted component is not disconnected, a backup protection system comes into operation. The backup operation isolates a larger segment and disturbs the system more than if the designated protection system had responded appropriately. An example is the protective relay systems for electric power transmission and distribution systems. A measure of system unreadiness is defined and suitable expressions are derived for this index and the mean duration of undetected faults. The unreadiness probability is sensitive not only to the failure rate of the protection system but also to the failure rate of the system, the pdf of interval between inspections and its mean value. The unreadiness probability estimated from a given system cannot be used for another system having a similar protection system. The protection system failure rate can, however, be used t~ obtain the unreadiness probability.
Operational availability of a multi-component sequential system with imperfect switching and opportunistic repair under preemptive resume repair policy. ANIL KUMAR. The QR JI (India) p. 81 (May 1980). In this paper, various aspects of reliability of a complex system consisting of three subsystems, in series, with a single repair facility, have been evaluated. One is made up of two identically redundant units whereas, the others are different, independent, repairable units. The repairs are carried out under priority basis. Concepts of shelf-life, opportunistic repair and imperfect sensor/switch have been introduced. "Inclusion of supplementary variable" and Laplace transforms techniques have been used to evaluate the operational availability of the system. A new bad data detection and identification algorithm. R. DORAISWAMIand J. L. R. PEREIRA. IEEE Trans. Reliab. R-29 (4). 333 {October 1980). A fast and reliable algorithm for detecting and identifying bad data in active-power measurements is proposed. The states are chosen to be the bus-voltage magnitudes and bus-voltage angle-differences. The bus-voltage angle-differences satisfy a set of loop equations derived from the topology of the network and a set of power-flow equations at nodes with no generations and loads. The algorithm is based on inspecting a similar set of equations. The proposed algorithm is tested on a realistic example and the results are compared with those of the conventional bad-data elimination scheme. System effectiveness models: an annotated bibliography. F. A. TILLMAN,C. L. HWANG and WAY KuO. IEEE Trans. Reliab. R-29 (4), 295 {October 1980). A state-of-the-art survey on system effectiveness models is presented. A variety of system
Availability analysis of a fault-tolerant computer system. MARIO DAL GIN. IEEE Trans, Reliab. R-29 (3), 265 (August 1980). Queueing network theory is applied in order to determine the availability of a fault-tolerant multiprocessor computer system. Mean repair times and utilizations of system units are computed. Subsystem availabilities are obtained by the decomposition technique of queueing network theory.
Hazard modification techniques for repair and multiple operating conditions. W. J. KOLARIK and K. E. CASE. IEEE Trans. Reliab. R-29 {4), 324 {October 1980). Two modeling techniques for simulating availability are developed. A technique is developed for introducing multiple operating conditions into simulation models. Another technique is developed for incorporating an operating-time adjustment for repair in availability simulations. A general operatingtime adjustment-technique is developed and then two special cases of this technique are discussed. The two modeling techniques developed in this paper allow the availability engineer to obtain more detail in availability studies. These techniques were used in the Farm Machinery Availability Cost Simulation Model. A review of standby redundant systems. ASHOK KUMAR and MANJU AGARWAL.IEEE Trans. Reliab. R-29 (4), 290 (October 1980). This article critically reviews the existing literature on standby redundant systems. Concepts related to standby systems have been defined. Parameters in which system designers and engineers may be interested have been discussed. Some of these parameters are mean-time-to-system failure, s-expected number of visits to a state, steady-state availability, and s-expected profit rate of the system. The types of systems discussed in literature and various methods employed by different workers in the analysis have been