Featured　Application　The　research　in　this　paper　will　serve　as　a　remarkable　guide　for　the　state　detection　and　remaining　useful　life　prediction　of　water　hydraulic　high-speed　on/off　valves　and　other　solenoid　valves.　Some　water　hydraulic　manipulators　used　for　the　remote　operation　of　reactors　are　controlled　by　a　high-speed　on/off　valve　(HSV).　Water　hydraulic　HSVs　operate　through　a　process　of　high-frequency　switching,　and　since　their　work　environment　is　poorly　lubricated,　their　components　are　prone　to　failure.　The　present　study　proposed　a　hybrid　model　to　detect　the　state　and　predict　the　RUL　of　water　hydraulic　HSVs　used　for　manipulators,　including　(1)　an　HSV　state　detection　method　based　on　the　fuzzy　neural　network　(FNN)　algorithm;　(2)　a　remaining　useful　life　(RUL)　prediction　method　based　on　the　integration　between　the　autoregressive　integrated　moving　average　(ARIMA)　model　and　the　long　short-term　memory　(LSTM)　model.　Final　results　showed　that　the　accuracy　of　state　detection　based　on　the　FNN　method　was　93.3%.　The　relative　error　of　the　RUL　prediction　based　on　the　ARIMA-LSTM　was　less　than　1.6%.　The　developed　method　can　provide　guidance　for　operation　and　maintenance　personnel　to　plan　maintenance　reasonably.