An　interval-fuzzy　integer　nonlinear　programming　(IFINP)　method　is　developed　for　the　identification　of　filter　allocation　and　replacement　strategies　in　a　fluid　power　system　(FPS)　under　uncertainty.　It　can　handle　uncertainties　expressed　as　interval-fuzzy　values　that　exist　in　the　left-　and　right-hand　sides　of　constraints　as　well　as　in　the　objective　function.　The　developed　method　is　applied　to　a　case　of　planning　filter　allocation　and　replacement　strategies　under　uncertainty　for　a　FPS　with　a　single　circuit.　A　piecewise　linearisation　approach　is　used　to　convert　the　nonlinear　problem　of　FPS　into　a　linear　one.　The　generated　fuzzy　solutions　will　be　used　to　analyse　and　interpret　the　multiple　decision　alternatives　under　various　system　conditions,　and　thus　help　decision-makers　to　make　a　compromise　among　the　system　contamination　level,　system　cost,　satisfaction　degrees　and　system-failure　risks　under　different　contaminant　ingression/generation　rates.　The　results　demonstrate　that　the　suction　and　return　filters　can　effectively　reduce　the　contamination　level　associated　with　a　low　system　cost,　but　the　FPS　will　take　lots　of　failure　risk　when　the　contaminant　ingression/generation　rate　is　high;　and　the　combination　of　suction　and　pressure　filters　can　bring　the　lowest　system　cost　with　more　security　instead.　Furthermore,　comparisons　for　the　optimised　solutions　are　made　among　IFINP,　interval-parameter　integer　nonlinear　programming　and　deterministic　linear　programming　also.　Generally,　the　IFINP　method　can　effectively　reduce　the　total　design　and　operation　cost　of　the　filtration　system　when　contaminants　ingression/generation　rate　is　high,　and　it　could　be　extended　to　the　lubricating　system.