Remaining　useful　life　(RUL)　prediction　of　mechanical　components　is　of　high　research　value　in　the　field　of　prognostics　and　health　management　(PHM).　However,　RUL　prediction　problems　are　completely　challenging　due　to　the　complicacy　of　bearings＇　operating　environment.　In　this　paper,　we　transform　the　vibration　acceleration　signal　collected　by　sensors　into　a　time-frequency　domain　matrix　through　continuous　wavelet　transform　(CWT)　and　then　extract　the　features　of　the　time-frequency　domain　matrix　through　the　proposed　multiscale　residual　convolutional　neural　network　(MRCNN),　which　enables　the　model　to　extract　more　local　and　global　features　while　constructing　more　accurate　health　indicators　(HI).　In　order　to　highlight　the　degradation　trend　of　mechanical　components,　the　obtained　health　indicators　are　smoothed　by　exponential　moving　average　(EMA).　Finally,　linear　regression　is　exploited　to　predict　the　RUL　of　the　bearing.　Performance　evaluations　based　on　the　public　dataset　PRONOSTIA　demonstrate　the　effectiveness　of　our　proposed　algorithm,　which　is　superior　to　existing　data-driven　algorithms　in　terms　of　prediction　accuracy.