System reliability is an important parameter in the operation of modern utility systems, spacecraft and manufacturing facilities. Over the last several decades researchers have used many different methods to determine complex system reliability. This paper uses new and novel techniques which are based on artificial intelligence and expert systems to determine system reliability. This work is based on heuristic search and a symbolic logic system which provides symbolic representation for overall system reliability when there is a single input and single output. Pivotal decomposition is used on a recursive basis to repeatedly reduce complex systems/subsystems to simpler systems. Eventually, these simpler systems are further reduced into easily resolved series-parallel arrangements. This symbolic logic system uses a heuristic based 'hill climbing' search algorithm. The algorithm identifies the pivotal or complex component. Once this pivotal component is selected, additional procedures are used to symbolically recognize other components within the resulting subgraphs which are made superfluous by the selection of the pivot component. These superfluous components are removed, and the simplification process is allowed to continue on a recursive basis. Once further recursive analysis is not needed, additional rules are employed to reduce the result to a system recognizable form in terms of series-parallel components. Finally, a 'symbolic processor' is used to convert this recognized form into a symbolic sum of products equation representing the overall system.
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering