The above requires rigorous modeling that, in addition, must be easily extendible and maintainable across organizational units. Moreover, the design process is iterative, involving both changing requirements and specifications along the different ways that lead to the set goal of the mission. The process is further complicated by the co-development of multiple solutions, each contributing differently to the goal and with different trade-offs. Space system engineering has to follow a rigorous design process to manage performance/risk trade-offs at each development stage and possibly across several functional and organizational domains. The obtained results allow the identification of the most impacting aspects on dependability analysis and provide valuable inputs to support the technological choices in terms of communication to implement virtual coupling. In particular, the impact of various communication parameters is scrutinized while taking into account different operational scenarios. Dependability evaluation is then performed by means of simulation. Namely, Stochastic Colored Petri Net (SCPN) models are developed to depict the exchanges of the various information needed under virtual coupling operation. In the present paper, we perform a model-based dependability analysis of the wireless communication system under virtual coupling operation. Train-to-Train (T2T) and Train-to-Ground (T2G) wireless communications will be the backbone for the implementation of the virtual coupling functionalities.
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It further handles the issue of assuming transition probabilities among the system states by calculating them using Petri net transitions’ throughput.Ī new paradigm based on the concept of virtual coupling of train sets is being elaborated to provide innovative solutions so as to increase efficiency, operational flexibility, line capacity, competitiveness among market players and quality of consumer experience.
: The method discussed in this paper presents a step of performing structural analysis on the Petri net of the system under consideration to identify and handle dead markings on the Petri net. To further validate and generalize the results, sensitivity analysis is performed by varying different system parameters. The predicted reliability of the system using this method is 99.99966% which has been validated using the specified system requirements. : The presented method has been applied to a safety-critical system, Shut Down System-1, of a nuclear power plant, which is operational in the Canada Deuterium Uranium reactor. At last, a Markov model is generated based on the reachability graph of the Petri net, which is further used to predict the system reliability. Thereafter, dead markings are identified in the Petri net which are further removed to perform steady-state analysis. : Initially, a generalized stochastic Petri net of the system under consideration is generated from the standard system's specification. The state transition probabilities are calculated using Petri net transitions’ throughput by performing stationary analysis under the consideration to identify and handle dead markings in the Petri net. The key idea is to violate the assumption of state transition probabilities in the Markov chain.
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: The objective of this paper is to present a method for predicting the reliability of software in industrial systems using a generalized stochastic Petri nets based approach. These probabilities are usually assumed or are obtained from the operational profile for which the system should be used in the field.
On the other hand, state-based models such as Markov chains suffer from the state-space explosion problem, and they need transition probability among different system states to measure reliability. have limitations in modeling complex system behavior. The combinatorial models used to predict reliability using architectures such as fault trees, binary decision diagrams, etc. : Software reliability prediction in the early stages of development can be propitious in many ways.
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