Noise　source　identification　of　gas　turbines　can　provide　the　basis　and　guidance　for　vibration　and　noise　reduction　of　gas　turbines.　Independent　component　analysis　(ICA)　is　one　of　the　most　popular　techniques　for　blind　source　separation　(BSS)　widely　used　in　vibration　and　noise　source　separation　in　mechanical　systems.　ICA　is　suitable　for　independent　source　signals.　However,　in　order　to　identify　dependent　mechanical　noise　sources　in　gas　turbines,　a　convolutive　BSS　in　the　frequency　domain　based　on　bounded　component　analysis　(BCA)　is　proposed.　First,　the　basic　theory　of　BSS　and　BCA　is　introduced　in　detail.　The　convolutive　mixing　in　the　time　domain　is　transformed　into　an　instantaneous　mixing　in　the　frequency　domain　by　short　time　Fourier　transform　(STFT),　and　complex　BCA　is　performed　at　each　frequency　bin.　Second,　a　permutation　alignment　method　based　on　local　and　global　optimization　is　proposed　to　solve　the　problem　of　the　permutation　ambiguity.　Finally,　the　accuracy　and　robustness　of　the　proposed　method　are　comparatively　studied　through　typical　numerical　and　experimental　studies　on　a　three-rotor　experimental　bench.　The　results　show　that　the　proposed　method　can　effectively　separate　and　identify　independent　and　dependent　source　signals.