Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925809
D. Huffman, R. Bergman, S. Amari, M. Zuo
In many practical cases, during a system failure or downtime, all non-failed components are kept idle to eliminate further damage to the system. This phenomenon is known as suspended animation (SA) because the aging process of the non-failed components is suspended. Suspended animation introduces dependencies among the component states. Therefore, we cannot calculate the system availability using the methods that are used to calculate the system reliability. In this paper, we provide a simple and efficient method to compute the availability indices of repairable systems subject to suspended animation. Using this method, we propose efficient algorithms for k-out-of-n systems. An important aspect of the proposed method is that it is not restricted to exponential failure and repair distributions. This method is also applicable for certain imperfect repair situations. Further, it can be applied to any system configuration with embedded hierarchical k-out-of-n subsystems subjected to suspended animation.
{"title":"Availability analysis of systems with suspended animation","authors":"D. Huffman, R. Bergman, S. Amari, M. Zuo","doi":"10.1109/RAMS.2008.4925809","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925809","url":null,"abstract":"In many practical cases, during a system failure or downtime, all non-failed components are kept idle to eliminate further damage to the system. This phenomenon is known as suspended animation (SA) because the aging process of the non-failed components is suspended. Suspended animation introduces dependencies among the component states. Therefore, we cannot calculate the system availability using the methods that are used to calculate the system reliability. In this paper, we provide a simple and efficient method to compute the availability indices of repairable systems subject to suspended animation. Using this method, we propose efficient algorithms for k-out-of-n systems. An important aspect of the proposed method is that it is not restricted to exponential failure and repair distributions. This method is also applicable for certain imperfect repair situations. Further, it can be applied to any system configuration with embedded hierarchical k-out-of-n subsystems subjected to suspended animation.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132866485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925777
Guo Li, Jianmin Gao, Fumin Chen
Causality diagram is one of the most effective theoretical models for uncertainty knowledge expression and reasoning. In order to realize the computer-aided construction of the causality diagram for diagnostics, this paper proposes a simple, yet comprehensive representation model to organize the failure knowledge more systematically and completely. This method defines the failure modes as the inherent properties of the physical entities at different hierarchical levels, and employs the polychromatic sets theory to represent the failure modes in terms of their interrelationships and their relations to the physical system. Then, the infrastructure of the diagnostic causality diagram (DCD) is constructed in a deductive manner by using the iterative search process operated on the reasoning matrices of the polychromatic sets. A case study is used to illustrate the feasibility of the proposed method. The research shows that the polychromatic sets approach to the DCD construction has formed mathematical foundation and can be readily implemented in computer.
{"title":"Construction of causality diagram model for diagnostics","authors":"Guo Li, Jianmin Gao, Fumin Chen","doi":"10.1109/RAMS.2008.4925777","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925777","url":null,"abstract":"Causality diagram is one of the most effective theoretical models for uncertainty knowledge expression and reasoning. In order to realize the computer-aided construction of the causality diagram for diagnostics, this paper proposes a simple, yet comprehensive representation model to organize the failure knowledge more systematically and completely. This method defines the failure modes as the inherent properties of the physical entities at different hierarchical levels, and employs the polychromatic sets theory to represent the failure modes in terms of their interrelationships and their relations to the physical system. Then, the infrastructure of the diagnostic causality diagram (DCD) is constructed in a deductive manner by using the iterative search process operated on the reasoning matrices of the polychromatic sets. A case study is used to illustrate the feasibility of the proposed method. The research shows that the polychromatic sets approach to the DCD construction has formed mathematical foundation and can be readily implemented in computer.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121143786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925846
E. Deloux, B. Castanier, C. Bérenguer
This paper deals with the maintenance optimization of a system subject to a stressful environment. The behavior of system deterioration can be modified by the environment. Reciprocally, the environment condition can be influenced by the system state and so, a change in the environment can be an indicator of the system state. We propose a condition-based maintenance decision framework to tackle the potential variations in the system deterioration, and especially in the deterioration rate, and the new information on the system state given by the evolution of the environmental variable. In this work, a degradation model is first developed to integrate the reciprocal influence on the system behaviour and the environment. A specific maintenance policy is constructed which combines a classical condition-based maintenance policy for the system state with a condition monitoring method to track the environmental changes. A long-run maintenance cost criteria is developed and numerical experiments are provided to highlight the benefits of our approach. The main conclusion of this study is the necessity to take into account the environment influence on the system state and we provide here an adequate maintenance framework for the decision-maker.
{"title":"Maintenance policy for a non-stationary deteriorating system","authors":"E. Deloux, B. Castanier, C. Bérenguer","doi":"10.1109/RAMS.2008.4925846","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925846","url":null,"abstract":"This paper deals with the maintenance optimization of a system subject to a stressful environment. The behavior of system deterioration can be modified by the environment. Reciprocally, the environment condition can be influenced by the system state and so, a change in the environment can be an indicator of the system state. We propose a condition-based maintenance decision framework to tackle the potential variations in the system deterioration, and especially in the deterioration rate, and the new information on the system state given by the evolution of the environmental variable. In this work, a degradation model is first developed to integrate the reciprocal influence on the system behaviour and the environment. A specific maintenance policy is constructed which combines a classical condition-based maintenance policy for the system state with a condition monitoring method to track the environmental changes. A long-run maintenance cost criteria is developed and numerical experiments are provided to highlight the benefits of our approach. The main conclusion of this study is the necessity to take into account the environment influence on the system state and we provide here an adequate maintenance framework for the decision-maker.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122425037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925802
J. L. Cook, J. Ramírez-Márquez
The Mobile Ad-hoc Wireless Network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and as such only the mobile nodes comprise the network. Nodes communicate either directly or through other nodes. To do so each node acts as source, destination, and relay. The virtue of a MAWN is the flexibility this provides however the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN's configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of mobility models. This paper expands upon these works by modifying the problem formulation to utilize a Monte Carlo simulation technique for the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints or limits on the networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of the method through the analysis of several example networks. Within this paper, a new and innovative use of the general MC simulation approach will be described that allows the practitioner to quickly approximate the reliability of a MAWN and understand the interactions of the characteristics that describe the MAWN; namely node reliability, node mobility, and transport (cluster) layer design. This paper is a follow on from one presented at RAMS 2007.
{"title":"Reliability for cluster-based Ad-hoc Networks","authors":"J. L. Cook, J. Ramírez-Márquez","doi":"10.1109/RAMS.2008.4925802","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925802","url":null,"abstract":"The Mobile Ad-hoc Wireless Network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and as such only the mobile nodes comprise the network. Nodes communicate either directly or through other nodes. To do so each node acts as source, destination, and relay. The virtue of a MAWN is the flexibility this provides however the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN's configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of mobility models. This paper expands upon these works by modifying the problem formulation to utilize a Monte Carlo simulation technique for the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints or limits on the networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of the method through the analysis of several example networks. Within this paper, a new and innovative use of the general MC simulation approach will be described that allows the practitioner to quickly approximate the reliability of a MAWN and understand the interactions of the characteristics that describe the MAWN; namely node reliability, node mobility, and transport (cluster) layer design. This paper is a follow on from one presented at RAMS 2007.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122469052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925781
Huairui Guo, R. Pan
Reliability demonstration tests for non-repairable systems have been extensively discussed by many researchers. However, very few works have been done for repairable system tests. Demonstration tests for repairable systems can be time consuming and costly. Carefully planning sample size and test duration is very important. This paper develops a theoretical method, based on pivotal quantities and a confidence bound requirement for the reliability metrics of interest, to help test planners to determine the minimal sample sizes and test duration. A case study was given and the developed theoretical results were compared with simulation results. The comparison shows that the proposed method is accurate and efficient.
{"title":"On determining sample size and testing duration of repairable system test","authors":"Huairui Guo, R. Pan","doi":"10.1109/RAMS.2008.4925781","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925781","url":null,"abstract":"Reliability demonstration tests for non-repairable systems have been extensively discussed by many researchers. However, very few works have been done for repairable system tests. Demonstration tests for repairable systems can be time consuming and costly. Carefully planning sample size and test duration is very important. This paper develops a theoretical method, based on pivotal quantities and a confidence bound requirement for the reliability metrics of interest, to help test planners to determine the minimal sample sizes and test duration. A case study was given and the developed theoretical results were compared with simulation results. The comparison shows that the proposed method is accurate and efficient.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122140277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925836
S. Amari, Relex Robert Bergman
Load-sharing systems have several practical applications. In load-sharing systems, the failure of a component will result in a higher load on each of the surviving components, thereby inducing a higher failure rate for them. This introduces failure dependency among the load-sharing components, which in turn increases the complexity in analyzing these systems. Therefore, in spite of a wide range of applications for load-sharing systems, the methods for computing the reliability of load-sharing systems are limited. In this paper, we first discuss the modeling concepts of load-sharing systems and explain the role of accelerated life testing models in analyzing these systems. We also describe existing analysis methods and their limitations in analyzing load-sharing systems. In modeling load-sharing systems with general failure distributions, it is important to consider an appropriate model to incorporate the effects of loading history. In this paper, we explore using the cumulative exposure model to account for the effects of loading history. We present an efficient method to compute the reliability and mean life of k-out-of-n load-sharing systems with identical or non-identical components following general failure distributions. The method can solve large k-out-of-n systems in a short time. Further, we show how to use the existing computational procedures for solving stochastic reward models for solving load-sharing models. In addition to the exact solutions, we also propose efficient approximations and bounds that can be computed easily. The computational procedure and the bounds proposed in this paper help reliability engineers to accurately model the load-sharing systems that arise in many practical situations.
{"title":"Reliability analysis of k-out-of-n load-sharing systems","authors":"S. Amari, Relex Robert Bergman","doi":"10.1109/RAMS.2008.4925836","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925836","url":null,"abstract":"Load-sharing systems have several practical applications. In load-sharing systems, the failure of a component will result in a higher load on each of the surviving components, thereby inducing a higher failure rate for them. This introduces failure dependency among the load-sharing components, which in turn increases the complexity in analyzing these systems. Therefore, in spite of a wide range of applications for load-sharing systems, the methods for computing the reliability of load-sharing systems are limited. In this paper, we first discuss the modeling concepts of load-sharing systems and explain the role of accelerated life testing models in analyzing these systems. We also describe existing analysis methods and their limitations in analyzing load-sharing systems. In modeling load-sharing systems with general failure distributions, it is important to consider an appropriate model to incorporate the effects of loading history. In this paper, we explore using the cumulative exposure model to account for the effects of loading history. We present an efficient method to compute the reliability and mean life of k-out-of-n load-sharing systems with identical or non-identical components following general failure distributions. The method can solve large k-out-of-n systems in a short time. Further, we show how to use the existing computational procedures for solving stochastic reward models for solving load-sharing models. In addition to the exact solutions, we also propose efficient approximations and bounds that can be computed easily. The computational procedure and the bounds proposed in this paper help reliability engineers to accurately model the load-sharing systems that arise in many practical situations.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131817101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925848
F. Sautter, P.W. Jemison, C.M. Goes, J. Wooten
In the CBM environment, health or condition is a function of an individual component, not families of components. In order to understand how a component degrades to a level where maintenance has to be performed, it is essential that the health and condition of that component can be understood by its individual ID. Likewise, the maintenance actions that are driven by its condition must be understood at both the fleet and local levels. In order to achieve that level of understanding, effective metrics must be generated using the data that comes from a task based, UID enabled MMIS. To achieve this goal will require transformation, not modernization, of today's MMIS within the DoD.
{"title":"An integrated maintenance management information system is the key to enabling condition based maintenance","authors":"F. Sautter, P.W. Jemison, C.M. Goes, J. Wooten","doi":"10.1109/RAMS.2008.4925848","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925848","url":null,"abstract":"In the CBM environment, health or condition is a function of an individual component, not families of components. In order to understand how a component degrades to a level where maintenance has to be performed, it is essential that the health and condition of that component can be understood by its individual ID. Likewise, the maintenance actions that are driven by its condition must be understood at both the fleet and local levels. In order to achieve that level of understanding, effective metrics must be generated using the data that comes from a task based, UID enabled MMIS. To achieve this goal will require transformation, not modernization, of today's MMIS within the DoD.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125612480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925834
Pingjian Yu, J. Song, C. R. Cassady
Estimation of reliability and maintainability parameters is essential in modeling repairable systems and determining maintenance policies. However, because of the aging of repairable systems under imperfect maintenance, failure times are neither identically nor independently distributed, which makes parameter estimation difficult. In this paper, we apply Bayesian methods for estimation of reliability and maintainability parameters based on historical reliability and maintainability (RAM) data. We assume the first failure of the repairable system follows a Weibull probability distribution. The repairable system experiences Kijima Type I imperfect corrective maintenance and Kijima Type I imperfect preventive maintenance. Using a Bayesian perspective, we estimate four parameters for this repairable system: the shape parameter of the Weibull probability distribution (beta), the scale parameter of the Weibull distribution (eta), the imperfect maintenance factor for corrective maintenance (alphar) and the imperfect maintenance factor for preventive maintenance (alphap). The proposed method is illustrated with simulated RAM data.
{"title":"Parameter estimation for a repairable system under imperfect maintenance","authors":"Pingjian Yu, J. Song, C. R. Cassady","doi":"10.1109/RAMS.2008.4925834","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925834","url":null,"abstract":"Estimation of reliability and maintainability parameters is essential in modeling repairable systems and determining maintenance policies. However, because of the aging of repairable systems under imperfect maintenance, failure times are neither identically nor independently distributed, which makes parameter estimation difficult. In this paper, we apply Bayesian methods for estimation of reliability and maintainability parameters based on historical reliability and maintainability (RAM) data. We assume the first failure of the repairable system follows a Weibull probability distribution. The repairable system experiences Kijima Type I imperfect corrective maintenance and Kijima Type I imperfect preventive maintenance. Using a Bayesian perspective, we estimate four parameters for this repairable system: the shape parameter of the Weibull probability distribution (beta), the scale parameter of the Weibull distribution (eta), the imperfect maintenance factor for corrective maintenance (alphar) and the imperfect maintenance factor for preventive maintenance (alphap). The proposed method is illustrated with simulated RAM data.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126753050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925819
T. Bedford, R. Denning, M. Revie, L. Walls
With the support of the United Kingdom Ministry of Defence (MOD), the Bayes linear method has been used to support reliability decision making on two 'live' projects. This paper focuses on one of these projects and shows how the Bayes linear methodology has been used to support decision making. The construction of a Bayes linear model comprises three parts. First, we need to structure the model. Second, we need to populate the model with our decision maker's beliefs. Finally, we must analyze the model once observed data has become available. This paper will briefly discuss how this has been carried out.
{"title":"Applying Bayes linear methods to support reliability procurement decisions","authors":"T. Bedford, R. Denning, M. Revie, L. Walls","doi":"10.1109/RAMS.2008.4925819","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925819","url":null,"abstract":"With the support of the United Kingdom Ministry of Defence (MOD), the Bayes linear method has been used to support reliability decision making on two 'live' projects. This paper focuses on one of these projects and shows how the Bayes linear methodology has been used to support decision making. The construction of a Bayes linear model comprises three parts. First, we need to structure the model. Second, we need to populate the model with our decision maker's beliefs. Finally, we must analyze the model once observed data has become available. This paper will briefly discuss how this has been carried out.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129601057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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