Fault　diagnosis　of　rotating　machinery　is　an　attractive　yet　challenging　task.　This　paper　presents　a　novel　intelligent　fault　diagnosis　scheme　for　rotating　machinery　based　on　ensemble　dilated　convolutional　neural　networks.　The　novel　fault　diagnosis　framework　employs　a　model　training　strategy　based　on　early　stopping　optimization　to　ensemble　several　one-dimensional　dilated　convolutional　neural　networks　(1D-DCNNs).　By　varying　the　dilation　rate　of　the　1D-DCNN,　different　receptive　fields　can　be　obtained　to　extract　different　vibration　signal　features.　The　early　stopping　strategy　is　used　as　a　model　update　threshold　to　prevent　overfitting　and　save　computational　resources.　Ensemble　learning　uses　a　weighted　mechanism　to　combine　the　outputs　of　multiple　1D-DCNN　subclassifiers　with　different　dilation　rates　to　obtain　the　final　fault　diagnosis.　The　proposed　method　outperforms　existing　state-of-the-art　classical　machine　learning　and　deep　learning　methods　in　simulation　studies　and　diagnostic　experiments,　demonstrating　that　it　can　thoroughly　mine　fault　features　in　vibration　signals.　The　classification　results　further　show　that　the　EDCNN　model　can　effectively　and　accurately　identify　multiple　faults　and　outperform　existing　fault　detection　techniques.