The　safety　of　an　Internet　Data　Center　(IDC)　is　directly　determined　by　the　reliability　and　stability　of　its　chiller　system.　Thus,　combined　with　deep　learning　technology,　an　innovative　hybrid　fault　diagnosis　approach　(1D-CNN_GRU)　based　on　the　time-series　sequences　is　proposed　in　this　study　for　the　chiller　system　using　1-Dimensional　Convolutional　Neural　Network　(1D-CNN)　and　Gated　Recurrent　Unit　(GRU).　Firstly,　1D-CNN　is　applied　to　automatically　extract　the　local　abstract　features　of　the　sensor　sequence　data.　Secondly,　GRU　with　long　and　short　term　memory　characteristics　is　applied　to　capture　the　global　features,　as　well　as　the　dynamic　information　of　the　sequence.　Moreover,　batch　normalization　and　dropout　are　introduced　to　accelerate　network　training　and　address　the　overfitting　issue.　The　effectiveness　and　reliability　of　the　proposed　hybrid　algorithm　are　assessed　on　the　RP-1043　dataset;　based　on　the　experimental　results,　1D-CNN_GRU　displays　the　best　performance　compared　with　the　other　state-of-the-art　algorithms.　Further,　the　experimental　results　reveal　that　1D-CNN_GRU　has　a　superior　identification　rate　for　minor　faults.