Machine　learning　(ML)　methods　are　becoming　popular　in　prognostics　and　health　management　(PHM)　of　engi-neering　systems　due　to　the　recent　advances　of　sensor　technology　and　the　prevalent　use　of　artificial　neural　net-works.　In　practice,　mechatronic　systems　are　by　nature,　prone　to　degradation/failure　due　to　complex　failure　mechanisms　and　other　unknown　causes.　As　a　result,　degradation　modeling　and　prediction　of　mechatronic　sys-tems　are　quite　challenging　especially　when　highly　integrative　and　special　operational　conditions　are　considered.　To　overcome　such　challenges,　artificial　neural　networks　can　be　employed.　This　paper　proposes　the　use　of　a　long　short-term　memory　(LSTM)-based　multi-input　neural　network　for　degradation　modeling　and　prediction　of　an　Electro-Hydrostatic　Actuator　(EHA)　system.　The　failure　mechanisms　of　the　EHA　system　are　explored　first,　and　the　obtained　physics-of-failure　information　is　utilized　in　constructing　the　LSTM　neural　network　to　enhance　the　prediction　capability　of　the　model.　An　actual　dataset　collected　from　an　EHA　test　bench　is　utilized　to　illustrate　the　effectiveness　of　the　proposed　physics-informed　LSTM　method　for　modeling　the　EHA　system＇s　degradation　behavior.　The　result　shows　that　the　proposed　method　provides　more　accurate　life　prediction　than　several　benchmark　methods　for　the　EHA　system.