System level diagnosis is an abstraction of high level and, thus, its practical implementation to particular cases of complex systems is the task which requires additional investigations, both theoretical and modeling. Traditionally, system self-diagnosis is used for detecting of permanently faulty nodes. In the paper, we consider the problems of intermittent fault detection and suggest diagnosis procedures which allow distinguishing between different types of intermittent faults. For each type of intermittent faults the diagnosis procedure was developed

No abstract

System level diagnosis is an abstraction of high level and, thus, its practical implementation to particular cases of complex systems is the task which requires additional investigations, both theoretical and modeling. Traditionally, system self-diagnosis is used for detecting of permanently faulty nodes. In the paper, we consider the problems of intermittent fault detection and suggest diagnosis procedures which allow distinguishing between different types of intermittent faults. For each type of intermittent faults the diagnosis procedure was developed

The paper deals with the problem of developing probabilistic algorithm for system level self-diagnosis. The main goal of the suggested algorithm is to minimize the mean time of its executing. The algorithm is based on the computing of the posterior probability of fault-free state of each system unit. Final decision about unit's state is made on the chosen decision rule. The execution of the probabilistic algorithm is elucidated with the help of simple example and then explained for the case of more complex systems.

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