Fault　diagnosis　of　rotating　machinery　plays　a　significant　role　for　the　reliability　and　safety　of　modern　industrial　systems.　As　an　emerging　field　in　industrial　applications　and　an　effective　solution　for　fault　recognition,　artificial　intelligence　(AI)　techniques　have　been　receiving　increasing　attention　from　academia　and　industry.　However,　great　challenges　are　met　by　the　AI　methods　under　the　different　real　operating　conditions.　This　paper　attempts　to　present　a　comprehensive　review　of　AI　algorithms　in　rotating　machinery　fault　diagnosis,　from　both　the　views　of　theory　background　and　industrial　applications.　A　brief　introduction　of　different　AI　algorithms　is　presented　first,　including　the　following　methods:　k-nearest　neighbour,　naive　Bayes,　support　vector　machine,　artificial　neural　network　and　deep　learning.　Then,　a　broad　literature　survey　of　these　AI　algorithms　in　industrial　applications　is　given.　Finally,　the　advantages,　limitations,　practical　implications　of　different　AI　algorithms,　as　well　as　some　new　research　trends,　are　discussed.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.