This　paper　aims　to　propose　a　more　efficient　classifier　and　applies　it　to　parametric　fault　diagnosis　for　converter　circuit.　A　novel　method　for　fault　diagnosis　based　on　a　fuzzy　cerebellar　model　neural　network　(FCMNN)　in　a　dual-buck　bidirectional　dc-ac　converter　circuit　is　proposed.　The　proposed　method　uses　fast　Fourier　transform　to　analyze　the　fault　signal　and　effectively　extract　fault　characteristics.　The　parameter　adaptation　laws　of　the　classifier　are　derived　to　achieve　fast　and　effective　training　and　testing　efficiency.　After　training,　the　neural　network　can　identify　the　working　state　of　capacitor　and　inductor,　and　achieve　parametric　fault　diagnosis.　The　training　samples　and　test　samples　are　collected　through　a　dual-buck　bidirectional　dc-ac　converter　circuit　simulation　system　and　a　practical　experimental　platform.　A　back-propagation　neural　network,　support　vector　machine,　and　the　proposed　FCMNN　are　performed　for　circuit　fault　diagnosis,　and　the　results　of　these　three　methods　are　compared.　Both　the　simulation　results　and　experimental　results　show　that　the　proposed　FCMNN　can　effectively　diagnose　parametric　faults,　such　as　the　component　degradation　of　the　capacitor　and　inductor,　with　fast　learning　and　diagnosis　speed,　higher　diagnostic　accuracy,　and　good　antinoise　ability.