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Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems
This paper discusses the application of fuzzy reasoning spiking
neural P systems with real numbers (rFRSN P systems) to fault
diagnosis of electric locomotive systems. Relationships among breakdown
signals and faulty sections in subsystems of electric locomotive systems
are described in the form of fuzzy production rules firstly and then fault
diagnosis models based on rFRSN P systems for these subsystems are
built according to these rules. Fuzzy production rules for diagnosing electric
locomotive systems are abstracted from the fault diagnosis analysis
of the subsystems and the causality among faulty sections, faulty subsystems
and electric locomotive systems. Finally, a diagnosis model based
on rFRSN P systems for electric locomotive systems is proposed.Ministerio de Econom铆a y Competitividad TIN2012-3743