Machine　learning,　especially　deep　learning,　has　been　extensively　applied　and　studied　in　the　area　of　machine　health　monitoring.　For　machine　health　monitoring　systems　(MHMS),　major　efforts　have　been　put　into　designing　and　deploying　more　and　more　complex　machine　learning　models.　Those　black-box　models　are　nontransparent　toward　their　working　mechanism.　However,　this　research　trend　brings　huge　potential　risks　in　real　life.　Since　machine　health　monitoring　itself　belongs　to　high　stake　decision　applications,　the　outputs　of　the　autonomous　monitoring　systems　should　be　trustworthy　and　reliable,　which　refers　to　obtain　explainability.　Then,　it　comes　to　the　following　key　question:　why　the　deployed　MHMS　predicts　what　they　predict.　In　this　article,　we　shed　some　light　on　this　meaningful　research　direction:　explainable　MHMSs　(EMHMS).　In　EMHMS,　the　machine　doctor　could　act　like　a　real　doctor　who　can　not　only　make　a　diagnosis　but　also　describe　the　patient＇s　symptoms.　First,　we　propose　a　specific　convolutional　neural　network　(CNN)　structure,　named　DecouplEd　Feature-Temporal　CNN　(DEFT-CNN),　to　balance　precision-explainability　tradeoff.　Specifically,　feature　information　and　temporal　information　have　been　encoded　in　different　stages　of　our　model.　The　spatial　attention　module　is　added　to　boost　the　performance　of　the　model.　Then,　to　explain　the　decision　of　the　model,　we　adopt　gradient-based　methods　to　generate　features　and　temporal　saliency　maps　highlighting　which　kinds　of　features　and　time　steps　are　keys　for　the　model＇s　predictions.　Finally,　we　conduct　the　experimental　studies　on　two　real　datasets　to　verify　the　effectiveness　of　our　proposed　framework.