Spiking neural P systems (SN P systems) have been well established as a novel class of distributed
parallel computing models. Some features that SN P systems possess are attractive
to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required
for many fault diagnosis applications. The lack of ability is a major problem of existing SN P
systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by
introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing
mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems).
The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis
knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning
algorithm based on FRSN P systems is developed according to neuron’s dynamic firing mechanism.
Besides, a practical example of transformer fault diagnosis is used to demonstrate the
feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
