Sliding　bearings　are　a　key　component　of　bridges,　and　their　normal　performance　is　an　important　prerequisite　to　ensure　traffic　safety.　To　solve　the　problem　wherein　a　single　type　of　sensor　has　difficulty　in　effectively　predicting　the　wear　life　of　sliding　bearings,　this　paper　proposes　a　method　to　predict　the　sliding　bearing　wear　life　using　the　multitype　monitoring　data　of　the　bridge.　This　method　uses　the　vertical　acceleration　data　of　the　girder　with　high　sampling　frequency　to　calculate　the　bearing　cumulative　dynamic　displacement　under　vehicle　load　and　then　processes　the　data　collected　by　the　longitudinal　displacement　gauge　with　low　sampling　frequency　to　extract　the　bearing　cumulative　static　displacement　under　the　effect　of　temperature.　Next,　the　daily　bearing　cumulative　displacement　calculated　by　adding　them　is　taken　as　the　evaluation　index,　and　the　sliding　bearing　wear　life　is　predicted　based　on　the　reliability　analysis.　Finally,　the　proposed　method　is　verified　by　a　numerical　example　of　vehicle-bridge　interaction　and　real　bridge　monitoring　data.　The　obtained　results　show　that　the　proposed　method　can　be　used　to　estimate　the　bearing　cumulative　displacement　with　high　accuracy　and　can　effectively　predict　the　wear　life　of　sliding　bearings.　The　prediction　results　can　provide　an　important　reference　for　bridge　evaluation.