An　interval-fuzzy　chance-constrained　integer　programming　(IFCIP)　method　is　developed　for　contamination　control　of　a　fluid　power　system　(FPS)　under　uncertainty.　The　model　is　derived　by　incorporating　the　techniques　of　fuzzy　and　chance-constrained　programming　within　a　general　interval-optimization　framework.　It　can　tackle　uncertainties　presented　as　both　fuzzy　members　and　discrete　intervals.　The　developed　method　is　applied　to　a　case　of　a　one-year　contamination　control　planning　for　FPS.　Interval　solutions　associated　different　risk　levels　of　constraint　violation　are　obtained,　which　can　be　used　for　generating　decision　alternatives.　Generally,　willingness　to　take　a　higher　risk　of　constraint　violation　will　guarantee　a　lower　system　cost;　a　strong　desire　to　acquire　a　lower　risk　will　run　into　a　higher　system　cost.　Thus,　the　method　provides　not　only　decision　variable　solutions　presented　as　stable　intervals　but　also　the　associated　risk　levels　in　violating　the　system　constraints.