Recently,　deep　neural　network　(DNN)　models　work　incredibly　well,　and　edge　computing　has　achieved　great　success　in　real-world　scenarios,　such　as　fault　diagnosis　for　large-scale　rotational　machinery.　However,　DNN　training　takes　a　long　time　due　to　its　complex　calculation,　which　makes　it　difficult　to　optimize　and　retrain　models.　To　address　such　an　issue,　this　work　proposes　a　novel　fault　diagnosis　model　by　combining　binarized　DNNs　(BDNNs)　with　improved　random　forests　(RFs).　First,　a　BDNN-based　feature　extraction　method　with　binary　weights　and　activations　in　a　training　process　is　designed　to　reduce　the　model　runtime　without　losing　the　accuracy　of　feature　extraction.　Its　generated　features　are　used　to　train　an　RF-based　fault　classifier　to　relieve　the　information　loss　caused　by　binarization.　Second,　considering　the　possible　classification　accuracy　reduction　resulting　from　those　very　similar　binarized　features　of　two　instances　with　different　classes,　we　replace　a　Gini　index　with　ReliefF　as　the　attribute　evaluation　measure　in　training　RFs　to　further　enhance　the　separability　of　fault　features　extracted　by　BDNN　and　accordingly　improve　the　fault　identification　accuracy.　Third,　an　edge　computing-based　fault　diagnosis　mode　is　proposed　to　increase　diagnostic　efficiency,　where　our　diagnosis　model　is　deployed　distributedly　on　a　number　of　edge　nodes　close　to　the　end　rotational　machines　in　distinct　locations.　Extensive　experiments　are　conducted　to　validate　the　proposed　method　on　the　data　sets　from　rolling　element　bearings,　and　the　results　demonstrate　that,　in　almost　all　cases,　its　diagnostic　accuracy　is　competitive　to　the　state-of-the-art　DNNs　and　even　higher　due　to　a　form　of　regularization　in　some　cases.　Benefited　from　the　relatively　lower　computing　and　storage　requirements　of　BDNNs,　it　is　easy　to　be　deployed　on　edge　nodes　to　realize　real-time　fault　diagnosis　concurrently.