Data-driven　methods　based　on　deep　neural　networks　(DNN)　are　widely　employed　for　predicting　the　remaining　useful　life　(RUL)　of　equipment,　yielding　remarkable　results.　However,　the　performance　of　DNN　relies　on　the　availability　and　completeness　of　full　lifecycle　data.　Moreover,　problems　such　as　lack　of　interpretability　of　prediction　results　and　weak　model　generalizability　still　exist.　A　RUL　prediction　approach　based　on　data　model　fusion　is　proposed　in　this　paper　to　address　these　problems.　This　approach　incorporates　physics　knowledge　into　the　stacked　bidirectional　long　short-term　memory　network　(SBiLSTM)　through　three　ways.　Firstly,　the　full　lifecycle　data　based　on　the　physics　degradation　model　is　integrated　with　sensed　data　to　ensure　the　integrity　of　degradation　data.　Secondly,　the　degradation　trajectory　simulated　based　on　the　physics　degradation　model　is　used　as　an　input　feature　for　the　SBiLSTM,　enabling　the　model　to　better　learn　the　state　evolution　process　of　the　equipment.　Moreover,　a　multi-objective　loss　function　is　constructed　by　introducing　a　physics-guided　inconsistency　loss　function　alongside　the　data　loss　function,　to　ensure　the　model　predictions　consistent　with　the　known　physics　phenomena　and　enhance　the　interpretability　of　the　model.　Case　studies　are　conducted　for　XJTU-SY　dataset　and　PHM2012　dataset　to　systematically　validate　the　proposed　approach.　Comparisons　with　existing　data-driven　and　hybrid　methods　are　made　and　the　results　consistently　demonstrate　the　accuracy　of　the　predictions　and　the　robustness　of　the　performance.　　©　2001-2012　IEEE.