In　this　study,　a　model　based　fault　diagnosis　method　for　drillstring　washout　is　proposed,　which　uses　iterated　unscented　Kalman　filter　(IUKF)　to　detect　the　emergence　of　drillstring　washout　and　to　estimate　washout　depth　and　washout　rate.　To　quantify　the　changes　in　pressure-loss　and　annulus　outlet　flow　rate　resulting　from　drillstring　washout,　pressure-loss　factors　and　flow　rate　factor　are　introduced　into　the　governing　equations　of　circulating　drilling　fluid　under　normal　drilling　condition.　Pressure-loss　factors　and　flow　rate　factor　are　estimated　by　IUKF　with　updated　measurements,　and　the　emergence　of　drillstring　washout　is　automatically　detected　by　confirming　the　changes　in　pressure-loss　factors　and　flow　rate　factor　using　generalized　likelihood　ratio　test　(GLRT).　After　drillstring　washout　is　detected,　continuously　updated　pressure　and　flow　rate　measurements　are　sent　to　the　identification　model　of　drillstring　washout　to　estimate　washout　depth　and　washout　rate　by　IUKF.　In　addition,　the　performances　of　fault　diagnosis　method　for　drillstring　washout　using　IUKF　and　unscented　Kalman　filter　(UKF)　are　contrasted.　Numerical　simulation　indicates　that　IUKF　and　UKF　have　equivalent　performance　in　drillstring　washout　detection,　while　IUKF　shows　a　better　performance　in　washout　depth　estimation.　When　using　IUKF,　the　average　relative　errors　of　estimated　washout　depth　and　washout　rate　are　2.1%　and　1.5%　respectively.　The　errors　of　estimated　washout　depth　and　washout　rate　are　2.8%　and　1.49%　when　using　UKF.　Robustness　analysis　indicates　that　IUKF　is　more　robust　than　UKF　to　measurement　noise.　IUKF　and　UKF　both　have　the　ability　to　estimate　washout　depth　and　washout　rate　within　the　noise　scope　of　0%-0.8%,　while　IUKF　is　more　accurate　than　UKF　for　washout　depth　estimation.