Electrical power has been the technological foundation of industrial societies for many years. Although the systems designed to provide and apply electrical energy have reached a high degree of maturity, unforeseen problems are constantly encountered, necessitating the design of more efficient and reliable systems based on novel technologies. The book series Power Systems is aimed at providing detailed, accurate and sound technical information about these new developments in electrical power engineering. It includes topics on power generation, storage and transmission as well as electrical machines. The monographs and advanced textbooks in this series address researchers, lecturers, industrial engineers and senior students in electrical engineering. ** Power Systems is indexed in Scopus** More information about this series at http://www.springer.com/series/4622

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Purpose – Since technological lifecycles do not always match hardware/software (HW/SW) lifecycles, obsolescence becomes a major issue in system lifecycle management as it can cause premature and unscheduled replacement of HW/SW subsystems. The purpose of this paper is to report a dynamic model to predict the obsolescence dates for HW/SW subsystems. Design/methodology/approach – The dynamic model estimates obsolescence dates for HW/SW subsystems based on graph theory concept. The model depicts the stages of subsystem obsolescence through transmittances composed of probability and time-distribution elements. The model predicts probability and mean time to obsolescence for line replaceable units (LRUs) over the lifetime of the system. An illustrative example in signaling systems used in a train control system was used to demonstrate the application of this model. Findings – Generally, the short timespan for HW/SW subsystems, which are periodically replaced with newer technologies, results in the development of new product lines by suppliers while they try to support legacy systems for a reasonable period of time. Obsolescence of HW/SW subsystems increases operation and maintenance costs as legacy systems are typically more expensive to maintain. The costs can be reduced by an optimum time to obsolescence derived from the model. Practical implications – This research adds to the body of knowledge on asset management and maintenance strategy. This paper may be of particular interest to reliability, maintainability and availability practitioners and project managers. Originality/value – The originality of this paper lies in developing a graph-based model that predicts probability and mean time to obsolescence for LRUs over the lifetime of the system.