A binary decision diagram method for phased mission analysis of non-repairable systems

Phased mission analysis is carried out to predict the reliability of systems which undergo a series of phases, each with differing requirements for success, with the mission objective being achieved only on the successful completion of all phases. Many systems from a range of industries experience such missions. The methods used for phased mission analysis are dependent upon the repairability of the system during the phases. If the system is non-repairable, fault-tree-based methods offer an efficient solution. For repairable systems, Markov approaches can be used. This paper is concerned with the analysis of non-repairable systems. When the phased mission failure causes are represented using fault trees, it is shown that the binary decision diagram (BDD) method of analysis offers advantages in the solution process. A new way in which BDD models can be efficiently developed for phased mission analysis is proposed. The paper presents a methodology by which the phased mission models can be developed and analysed to produce the phase failure modes and the phase failure likelihoods

High performance reliability analysis of phased mission systems

Systems often operate over a set of time periods, known as phases, in which their reliability structure varies and many include both repairable and nonrepairable components. Success for such systems is defined as the completion of all phases, known as a phased mission, without failure. An example of such a system is an aircraft landing gear system during a flight. The Binary Decision Diagram (BDD) method provides the most efficient solution to the unreliability of non-repairable systems whilst for repairable systems Markov or other state-space based methods have been most widely applied. For systems containing both repairable and non-repairable components the repairable modelling methods are normally used, despite having far higher computational expense than the non-repairable methods, since only they are able to handle the dependencies involved. This paper introduces improvements to the BDD method for analysing non-repairable systems as well as an entirely new method that utilises a new modelling technique involving both BDD and Markov techniques

A reliability analysis method using binary decision diagrams in phased mission planning

The use of autonomous systems is becoming increasingly common in many fields. A significant example of this is the ambition to deploy UAVs (unmanned aerial vehicles) for both civil and military applications. In order for autonomous systems such as these to operate effectively they must be capable of making decisions regarding the appropriate future course of their mission responding to changes in circumstance in as short a time as possible. The systems will typically perform phased missions and, due to the uncertain nature of the environments in which the systems operate, the mission objectives may be subject to change at short notice. The ability to evaluate the different possible mission configurations is crucial in making the right decision about the mission tasks that should be performed in order to give the highest possible probability of mission success. Since Binary Decision Diagrams (BDD) may be quickly and accurately quantified to give measures of the system reliability it is anticipated that they are the most appropriate analysis tools to form the basis of a reliability-based prognostics methodology. This paper presents a new Binary Decision Diagram based approach for phased mission analysis, which seeks to take advantage of the proven fast analysis characteristics of the BDD and enhance it in ways which are suited to the demands of a decision making capability for autonomous systems. The BDD approach presented allows BDDs representing the failure causes in the different phases of a mission to be constructed quickly by treating component failures in different phases of the mission as separate variables. This allows flexibility when building mission phase failure BDDs since a global variable ordering scheme is not required. An alternative representation of component states in time intervals allows the dependencies to be efficiently dealt with during the quantification process. Nodes in the BDD can represent components with any number of failure modes or factors external to the system that could affect its behaviour, such as the weather. Path simplification rules and quantification rules are developed that allow the calculation of phase failure probabilities for this new BDD approach. The proposed method provides a phased mission analysis technique that allows the rapid construction of reliability models for phased missions and, with the use of BDDs, rapid quantification

Phased mission analysis using the cause–consequence diagram method

Most reliability analysis techniques and tools assume that a system used for a mission consists of a single phase. However, multiple phases are natural in many missions. A system that can be modelled as a mission consisting of a sequence of phases is called a phased mission system. In this case, for successful completion of each phase the system may have to meet different requirements. System failure during any phase will result in mission failure. Fault tree analysis, binary decision diagrams and Markov techniques have been used to model phased missions. The cause–consequence diagram method is an alternative technique capable of modelling all system outcomes (success and failure) in one logic diagram. [Continues.

A reliability analysis method using binary decision diagrams in phased mission planning

The use of autonomous systems is becoming increasingly common in many fields. A significant example of this is the ambition to deploy unmanned aerial vehicles (UAVs) for both civil and military applications. In order for autonomous systems such as these to operate effectively, they must be capable of making decisions regarding the appropriate future course of their mission responding to changes in circumstance in as short a time as possible. The systems will typically perform phased missions and, owing to the uncertain nature of the environments in which the systems operate, the mission objectives may be subject to change at short notice. The ability to evaluate the different possible mission configurations is crucial in making the right decision about the mission tasks that should be performed in order to give the highest possible probability of mission success. Because binary decision diagrams (BDDs) may be quickly and accurately quantified to give measures of the system reliability it is anticipated that they are the most appropriate analysis tools to form the basis of a reliability-based prognostics methodology. The current paper presents a new BDD-based approach for phased mission analysis, which seeks to take advantage of the proven fast analysis characteristics of the BDD and enhance it in ways that are suited to the demands of a decision-making capability for autonomous systems. The BDD approach presented allows BDDs representing the failure causes in the different phases of a mission to be constructed quickly by treating component failures in different phases of the mission as separate variables. This allows flexibility when building mission phase failure BDDs because a global variable ordering scheme is not required. An alternative representation of component states in time intervals allows the dependencies to be efficiently dealt with during the quantification process. Nodes in the BDD can represent components with any number of failure modes or factors external to the system that could affect its behaviour, such as the weather. Path simplification rules and quantification rules are developed that allow the calculation of phase failure probabilities for this new BDD approach. The proposed method provides a phased mission analysis technique that allows the rapid construction of reliability models for phased missions and, with the use of BDDs, rapid quantification

An efficient phased mission reliability analysis for autonomous vehicles

Autonomous systems are becoming more commonly used, especially in hazardous situations. Such systems are expected to make their own decisions about future actions when some capabilities degrade due to failures of their subsystems. Such decisions are made without human input, therefore they need to be well-informed in a short time when the situation is analysed and future consequences of the failure are estimated. The future planning of the mission should take account of the likelihood of mission failure. The reliability analysis for autonomous systems can be performed using the methodologies developed for phased mission analysis, where the causes of failure for each phase in the mission can be expressed by fault trees. Unmanned Autonomous Vehicles (UAVs) are of a particular interest in the aeronautical industry, where it is a long term ambition to operate them routinely in civil airspace. Safety is the main requirement for the UAV operation and the calculation of failure probability of each phase and the overall mission is the topic of this paper. When components or sub-systems fail or environmental conditions throughout the mission change, these changes can affect the future mission. The new proposed methodology takes into account the available diagnostics data and is used to predict future capabilities of the UAV in real-time. Since this methodology is based on the efficient BDD method, the quickly provided advice can be used in making decisions. When failures occur appropriate actions are required in order to preserve safety of the autonomous vehicle. The overall decision making strategy for autonomous vehicles is explained in this paper. Some limitations of the methodology are discussed and further improvements are presented based on experimental results

Methods for the efficient measurement of phased mission system reliability and component importance

An increasing number of systems operate over a number of consecutive time periods, in which their reliability structure and the consequences of failure differ, in order to perform some overall operation. Each distinct time period is known as a phase and the overall operation is known as a phased mission. Generally, a phased mission fails immediately if the system fails at any point and is considered a success only if all phases are completed without failure. The work presented in this thesis provides efficient methods for the prediction and optimisation of phased mission reliability. A number of techniques and methods for the analysis of phased mission reliability have been previously developed. Due to the component and system failure time dependencies introduced by the phases, the computational expense of these methods is high and this limits the size of the systems that can be analysed in reasonable time frames on modern computers. Two importance measures, which provide an index of the influence of each component on the system reliability, have also been previously developed. This is useful for the optimisation of the reliability of a phased mission, however a much larger number have been developed for non-phased missions and the different perspectives and functions they provide are advantageous. This thesis introduces new methods as well as improvements and extensions to existing methods for the analysis of both non-repairable and repairable systems with an emphasis on improved efficiency in the derivation of phase and mission reliability. New importance measures for phased missions are also presented, including interpretations of those currently available for non-phased missions. These provide a number of interpretations of component importance, allowing those most suitable in a given context to be employed and thus aiding in the optimisation of mission reliability. In addition, an extensive computer code has been produced that implements and tests the majority of the newly developed techniques and methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An efficient phased mission reliability analysis for autonomous vehicles

Autonomous systems are becoming more commonly used, especially in hazardous situations. Such systems are expected to make their own decisions about future actions when some capabilities degrade due to failures of their subsystems. Such decisions are made without human input, therefore they need to be well-informed in a short time when the situation is analysed and future consequences of the failure are estimated. The future planning of the mission should take account of the likelihood of mission failure. The reliability analysis for autonomous systems can be performed using the methodologies developed for phased mission analysis, where the causes of failure for each phase in the mission can be expressed by fault trees. Unmanned autonomous vehicles (UAVs) are of a particular interest in the aeronautical industry, where it is a long term ambition to operate them routinely in civil airspace. Safety is the main requirement for the UAV operation and the calculation of failure probability of each phase and the overall mission is the topic of this paper. When components or subsystems fail or environmental conditions throughout the mission change, these changes can affect the future mission. The new proposed methodology takes into account the available diagnostics data and is used to predict future capabilities of the UAV in real time. Since this methodology is based on the efficient BDD method, the quickly provided advice can be used in making decisions. When failures occur appropriate actions are required in order to preserve safety of the autonomous vehicle. The overall decision making strategy for autonomous vehicles is explained in this paper. Some limitations of the methodology are discussed and further improvements are presented based on experimental results

Methods for the efficient measurement of phased mission system reliability and component importance

An increasing number of systems operate over a number of consecutive time periods, in which their reliability structure and the consequences of failure differ, in order to perform some overall operation. Each distinct time period is known as a phase and the overall operation is known as a phased mission. Generally, a phased mission fails immediately if the system fails at any point and is considered a success only if all phases are completed without failure. The work presented in this thesis provides efficient methods for the prediction and optimisation of phased mission reliability. A number of techniques and methods for the analysis of phased mission reliability have been previously developed. Due to the component and system failure time dependencies introduced by the phases, the computational expense of these methods is high and this limits the size of the systems that can be analysed in reasonable time frames on modern computers. Two importance measures, which provide an index of the influence of each component on the system reliability, have also been previously developed. This is useful for the optimisation of the reliability of a phased mission, however a much larger number have been developed for non-phased missions and the different perspectives and functions they provide are advantageous. This thesis introduces new methods as well as improvements and extensions to existing methods for the analysis of both non-repairable and repairable systems with an emphasis on improved efficiency in the derivation of phase and mission reliability. New importance measures for phased missions are also presented, including interpretations of those currently available for non-phased missions. These provide a number of interpretations of component importance, allowing those most suitable in a given context to be employed and thus aiding in the optimisation of mission reliability. In addition, an extensive computer code has been produced that implements and tests the majority of the newly developed techniques and methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using reliability analysis to support decision making in phased mission systems

Due to the environments in which they will operate, future autonomous systems must be capable of reconfiguring quickly and safely following faults or environmental changes. Past research has shown how, by considering autonomous systems to perform phased missions, reliability analysis can support decision making by allowing comparison of the probability of success of different missions following reconfiguration. Binary Decision Diagrams (BDDs) offer fast, accurate reliability analysis that could contribute to real-time decision making. However, phased mission analysis using existing BDD models is too slow to contribute to the instant decisions needed in time-critical situations. This paper investigates two aspects of BDD models that affect analysis speed: variable ordering and quantification efficiency. Variable ordering affects BDD size, which directly affects analysis speed. Here, a new ordering scheme is proposed for use in the context of a decision making process. Variables are ordered before a mission and reordering is unnecessary no matter how the mission configuration changes. Three BDD models are proposed to address the efficiency and accuracy of existing models. The advantages of the developed ordering scheme and BDD models are demonstrated in the context of their application within a reliability analysis methodology used to support decision making in an Unmanned Aerial Vehicle