Eddy-current　testing　technique　has　been　extensively　explored　for　estimating　the　electromagnetic　property　of　steel　plates　in　various　industrial　applications.　In　this　article,　a　physics-guided　deep　learning　(DL)　method　is　proposed　to　estimate　the　permeability　of　plate　in　high　thickness　with　probe　liftoff.　A　simplified　analytical　model　is　derived,　which　realises　the　single　to　multiple　frequency　inductance　transformation　and　calculates　the　related　physical　properties　of　the　measurement　configuration.　A　constant　is　found,　which　is　a　fundamental　coefficient　describing　the　first-order　nature　of　the　sensor　response　to　a　plate　and　it　is　insensitive　to　plate　properties　and　probe　dimensions.　The　nonlinear　mapping　from　physical　information,　derived　from　the　simplified　analytical　model,　to　plate　permeability　is　constructed　by　the　DL　model　based　on　the　modified　ResNet18-1D.　Numerical　simulations　and　experiments　have　been　performed　to　evaluate　the　proposed　method　for　permeability　estimation　with　various　plate　materials　and　probe　liftoff.　The　method　achieves　real-time　accurate　estimation　of　plate　permeability　with　a　relative　error　lower　than　3%.　©　2005-2012　IEEE.