8,274 research outputs found
An enhanced component connection method for conversion of fault trees to binary decision diagrams
Fault Tree Analysis (FTA) is widely applied to assess the failure probability of industrial systems. Many computer packages are available which are based on conventional Kinetic Tree Theory methods. When dealing with large (possibly non-coherent) fault trees, the limitations of the technique in terms of accuracy of the solutions and the efficiency of the processing time becomes apparent. Over recent years the Binary Decision Diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. First of all, a fault tree for a particular system failure mode is constructed and then converted to a BDD for analysis. This paper analyses alternative methods for the fault tree to BDD conversion process.
For most fault tree to BDD conversion approaches the basic events of the fault tree are placed in an ordering. This can dramatically affect the size of the final BDD and the success of qualitative and quantitative analyses of the system. A set of rules are then applied to each gate in the fault tree to generate the BDD. An alternative approach can also be used, where BDD constructs for each of the gate types are first built and then merged to represent a parent gate. A powerful and efficient property, sub-node sharing, is also incorporated in the enhanced method proposed in this paper. Finally a combined approach is developed taking the best features of the alternative methods. The efficiency of the techniques is analysed and discussed
A Simple Component Connection Approach for Fault Tree Conversion to Binary
Fault Tree Analysis (FTA) is commonly used when
conducting risk assessments of industrial systems. A
number of computer packages based on conventional
analysis methods are available to perform the analysis.
However, dealing with large (possibly non-coherent) fault
trees can expose the limitations of the technique in terms
of accuracy of the solutions and the processing time
required. Over recent years the Binary Decision Diagram
(BDD) method has been developed for the solution of the
fault tree and overcomes the disadvantages of the
conventional FTA approaches. The usual way of taking
advantage of the BDD structure is to construct a fault
tree and then convert it to a BDD. This paper will focus
on the fault tree to BDD conversion process.
Converting the fault tree requires the basic events of
the fault tree to be placed in an ordering. This is critical
to the size of the final BDD and ultimately affects the
qualitative and quantitative analysis of the system and
benefits of this method. Once the ordering is established
several approaches can be used for the BDD generation.
One approach is to apply a set of rules developed by
Rauzy which are repeatedly applied to each gate in the
fault tree to generate the BDD. An alternative approach
can be used when BDD constructs for each of the gate
types are first built and then connected together. A subnode
sharing feature in the second of these approaches
and a third, hybrid, combined approach will be presented.
Some remarks on the effectiveness of these techniques will
be provided
Fault tree conversion to binary decision diagrams
Fault Tree Analysis is a commonly used technique to predict the causes of a specific system failure mode and to then determine the likelihood of this event. Over recent years the Binary Decision Diagram (BDD) method has been developed for the solution of the fault tree. It can be shown that this approach has advantages in terms of both accuracy and efficiency over the conventional method of analysis formulated in the 1970’s. The BDD expresses the failure logic in a disjoint form which gives it an advantage from the computational viewpoint. Fault Trees, however, remain the better way to represent the system failure causality. Therefore the usual way of taking advantage of the BDD structure is to construct a fault tree and then convert this to a BDD. It is on the fault tree conversion process
that this paper will focus.
In order to construct a BDD the variables which represent the occurrence of the basic events in the fault tree have to be placed in an ordering. Depending on the ordering selected an efficient representation of the failure logic can be obtained or if a poor ordering is selected a less efficient analysis will result. Once the ordering is established one approach is to utilise a set of rules developed by Rauzy which are repeatedly applied to generate the BDD. An
alternative approach can be used whereby BDD constructs for each of the gate types are first formed and then joined together as specified by the gates in the fault tree. Some comments on the effectiveness of these approaches will be provided
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
Choosing a heuristic for the “fault tree to binary decision diagram” conversion, using neural networks
Fault-tree analysis is commonly used for risk assessment
of industrial systems. Several computer packages are
available to carry out the analysis. Despite its common usage there
are associated limitations of the technique in terms of accuracy
and efficiency when dealing with large fault-tree structures. The
most recent approach to aid the analysis of the fault-tree diagram
is the BDD (binary decision diagram). To use the BDD, the
fault-tree structure needs to be converted into the BDD format.
Converting the fault tree is relatively straightforward but requires
that the basic events of the tree be ordered. This ordering is
critical to the resulting size of the BDD, and ultimately affects
the qualitative and quantitative performance and benefits of
this technique. Several heuristic approaches were developed to
produce an optimal ordering permutation for a specific tree. These
heuristic approaches do not always yield a minimal BDD structure
for all trees. There is no single heuristic that guarantees a minimal
BDD for any fault-tree structure. This paper looks at a selection
approach using a neural network to choose the best heuristic from
a set of alternatives that will yield the smallest BDD and promote
an efficient analysis. The set of possible selection choices are 6
alternative heuristics, and the prediction capacity produced was
a 70% chance of the neural network choosing the best ordering
heuristic from the set of 6 for the test set of given fault trees
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
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
Fault Tree Analysis: a survey of the state-of-the-art in modeling, analysis and tools
Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results
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