We demonstrate a method of generating an exact analytical expression for the reliability of a complex system using a directed acyclic graph to represent the system’s reliability block diagram. Additionally, we show how statistical information stored in a reliability block diagram can be used to transform an analytical expression into a time-dependent function for system reliability.
Among its many interpretations, the term reliability most commonly refers to the ability of a device or system to perform a task successfully when required. More formally, it is described as the probability of functioning properly at a given time and under specified operating conditions . Mathematically, the reliability function is defined by
where is a nonnegative random variable representing the device or system lifetime.
For a system composed of at least two components, the system reliability is determined by the reliability of the individual components and the relationships among them. These relationships can be depicted using a reliability block diagram (RBD).
Simple systems are usually represented by RBDs with components in either a series or parallel configuration. In a series system, all components must function satisfactorily in order for the system to operate. For a parallel system to operate, at least one component must function correctly. Systems can also contain components arranged in both series and parallel configurations. If an RBD cannot be reduced to a series, parallel, or series-parallel configuration, then it is considered a complex system.
This article deals with the generation of an exact analytical expression for the reliability of a complex system. The demonstrated method relies on finding all paths between the source and target vertices in a directed acyclic graph (i.e., RBD), as well as the inclusion-exclusion principle for probability.
A Note on Timings
The timings reported in this article were measured on a custom workstation PC using the built-in function Timing. The system consists of an Intel® Core i7 CPU 950 @ 4 GHz and 24 GB of DDR3 memory. It runs Microsoft® Windows 7 Professional (64-bit) and scores 1.32 on the MathematicaMark9 benchmark.
Finding All -Paths in a Directed Acyclic Graph
We begin by considering a directed graph that consists of a finite set of vertices together with a finite set of ordered pairs of vertices called directed edges. The built-in function Graph can be used to construct a graph from explicit lists of vertices and edges.
This two-dimensional grid graph, labeled , can be constructed much more efficiently by using the built-in function GridGraph. Throughout this section, we utilize it to illustrate our functions.
Now, for a vertex , we define the set of out-neighbors as
where is taken to mean a directed edge from to . This is implemented in the function VertexOutNeighbors.
VertexOutNeighbors behaves similarly to the built-in function VertexOutDegree. That is, given a graph and a vertex , the function returns a list of out-neighbors for the specified vertex.
If, however, only the graph is specified, the function will give a list of vertex out-neighbors for all vertices in the graph.
The order in which the out-neighbors are displayed is determined by the order of vertices returned by VertexList.
We can implement similar functions to obtain the set of in-neighbors by simply changing to .
The next step toward our goal is to consider a method of traversing a graph. One common approach of systematically visiting all vertices of a graph is known as depth-first search (DFS). In its most basic form, a DFS algorithm involves visiting a vertex, marking it as “visited,” and then recursively visiting all of its neighbors . The function DepthFirstSearch implements this algorithm for directed graphs.
Given a graph and a starting vertex , DepthFirstSearch returns a list of vertices in the order in which they are visited.
We compare this with the result of the built-in function DepthFirstScan.
Next, let us define the function DirectedAcyclicGraphQ.
If the graph is both directed and acyclic, DirectedAcyclicGraphQ yields True. Otherwise, it yields False.
Finally, we consider the problem of finding all paths in a directed acyclic graph between two arbitrary vertices . Typically, we refer to as the source and as the target. A path in is defined as a sequence of vertices such that for . Since we have constrained ourselves to a directed acyclic graph, all paths are simple. That is to say, all vertices in a path are distinct.
By modifying the depth-first search algorithm, we arrive at a solution.
Like the original DFS algorithm, we visit a vertex and then recursively visit all of its neighbors. However, instead of checking if a vertex has been marked “visited,” we compare the current vertex to the target. If they do not match, we continue to traverse the graph. Otherwise, the target has been reached and we store the path for later output.
For a given directed acyclic graph , a source vertex , and a target vertex , FindPaths returns a list of all paths connecting to .
In this particular instance, the function takes approximately 0.85 milliseconds to return the result.
FindPaths works for any pair of vertices.
If no path is found, the function returns an empty list.
Minimal Paths, Inclusion-Exclusion, and System Reliability
Up to this point, we have been working with graphs in an abstract, mathematical sense. We now make the transition from directed acyclic graph to reliability block diagram by associating vertices with components in a system and edges with relationships among them.
Consider a single component in an RBD. Let us imagine a “flow” moving from a source, through the component, to a target. The component is deemed to be functioning if the flow can pass through it unimpeded. However, if the component has failed, the flow is prevented from reaching the target.
The “flow” concept can be extended to an entire system. A system is considered to be functioning if there exists a set of functioning components that permits the flow to move from source to target. We define a path in an RBD as a set of functioning components that guarantees a functioning system. Since we have chosen to use a directed acyclic graph to represent a system’s RBD, all paths are minimal. That is to say, all components in a path are distinct.
Once the minimal paths of a system’s RBD have been obtained, the principle of inclusion-exclusion for probability can be employed to generate an exact analytical expression for reliability. Let be the set of all minimal paths of a system. At least one minimal path must function in order for the system to function. We can write the reliability of the system as the probability of the union of all minimal paths:
This is implemented in the function SystemReliability.
Given a system’s RBD (represented by a directed acyclic graph ), a source vertex , and a target vertex , SystemReliability returns an exact analytical expression for the reliability.
Consider the RBD of a simple system with four components in a series configuration.
The reliability of the system is given in terms of the reliability of its four components.
Consider the RBD of a simple system with four components in a parallel configuration.
The “start” and “end” components are not part of the actual system. They are added to ensure the RBD meets the criteria for a directed acyclic graph.
Furthermore, these nonphysical components are taken to have perfect reliability, that is, . Since they have no effect on the system’s reliability, they can be safely removed from the resulting analytical expression. To do so, we simply define a list of replacement rules and apply it to the result of SystemReliability.
The reliability of the system is given in terms of the reliability of its four components.
Next, we examine the RBDs of two simple systems with components in a series-parallel configuration.
Component is in series with component , and both components are in parallel with component .
As in previous examples, we use SystemReliability to obtain an exact analytical expression for the reliability.
Finally, we examine the RBDs of two complex systems.
The reliability of the system is given in terms of the reliability of its six components.
The result is returned after approximately 0.59 milliseconds.
The reliability of the system is given in terms of the reliability of its fourteen components.
The result is returned after approximately 0.33 seconds.
Time-Dependent Reliability of a Complex System
We now turn our attention to the derivation of a time-dependent expression for the reliability of a complex system based on information contained within its reliability block diagram.
Let us imagine that we have a generic system composed of six subsystems and we know the reliability relationships among them. In addition, the underlying statistical distributions and parameters used to model the subsystems’ reliabilities are known.
We begin by creating the system’s RBD.
In defining the RBD, we have made use of the Property function to store information associated with each subsystem. For instance, the custom property "Distribution" is used to store a parametric statistical distribution. Labels, images, and other properties can also be specified.
Next, we use SystemReliability to generate an exact analytical expression for the reliability.
Now, the reliability function of the subsystem is given by
where is the corresponding cumulative distribution function (CDF). For each subsystem, we use PropertyValue to extract the symbolic distribution stored in the RBD, and then use the built-in function CDF to construct its reliability function.
We extract additional information, for example, subsystem labels, from the RBD and combine it with the reliability functions to create plots for comparison.
In order to transform our static analytical expression into a time-dependent function, we first define a list of replacement rules.
Next, we apply the list of rules to the expression for system reliability.
The result is a time-dependent reliability function for the complex system described by the RBD.
Finally, we generate a plot of the system’s reliability over time.
Discussion and Conclusion
We have demonstrated a method of generating an exact analytical expression for the reliability of a complex system using a directed acyclic graph to represent the system’s reliability block diagram. In addition, we have shown how to convert an analytical expression for system reliability into a time-dependent function based on statistical information stored in an RBD. While our focus has been on the analysis of complex systems, we have also shown that the combination of path finding and the inclusion-exclusion principle is equally applicable to simple systems in series, parallel, or series-parallel configurations.
Knowing the static analytical expression or time-dependent solution of a system allows us to perform a more advanced reliability analysis. For instance, we can easily calculate the Birnbaum importance
of the component using the result of SystemReliability. Similarly, we can derive the hazard function, or failure rate, from the system’s time-dependent reliability function.
There are several ways in which the functionality demonstrated in this article can be improved and expanded:
- Increase the efficiency of SystemReliability by implementing improvements to the classical inclusion-exclusion principle .
- Add functions related to common tasks in reliability analysis, for example, reliability importance, failure rate, and so on.
- Add support for -out-of- structures, that is, redundancy.
- Add the ability to export and import complete RBDs.
- Add a mechanism, for example, a graphical user interface (GUI), to facilitate the construction and modification of RBDs.
Finally, the code can be combined into a user-friendly package with full documentation.
|||W. Kuo and M. Zuo, Optimal Reliability Modeling: Principles and Applications, Hoboken, NJ: John Wiley & Sons, 2003.|
|||S. Skiena, The Algorithm Design Manual, 2nd ed., London, UK: Springer-Verlag, 2008.|
|||K. Dohmen, “Improved Inclusion-Exclusion Identities and Inequalities Based on a Particular Class of Abstract Tubes,” Electronic Journal of Probability, 4, 1999 pp. 1-12. doi:10.1214/EJP.v4-42.|
|T. Silvestri, “Complex System Reliability,” The Mathematica Journal, 2014. dx.doi.org/doi:10.3888/tmj.16-7.|
About the Author
Todd Silvestri received his undergraduate degrees in physics and mathematics from the University of Chicago in 2001. As a graduate student, he worked briefly at the Thomas Jefferson National Accelerator Facility (TJNAF) where he helped to construct and test a neutron detector used in experiments to measure the neutron electric form factor at high momentum transfer. From 2006 to 2011, he worked as a physicist at the US Army Armament Research, Development and Engineering Center (ARDEC). During his time there, he cofounded and served as principal investigator of a small laboratory focused on improving the reliability of military systems. He is currently working on several personal projects.
New Jersey, United States