Intelligent　diagnosis　algorithms　can　monitor　faults　with　industrial　production　of　a　timely　manner　via　their　powerful　learning　ability.　Multi-sensor　diagnosis　systems　can　more　comprehensively　describe　the　state　of　equipment　and　avoid　the　influence　of　incorrect　data　acquisition　locations,　which　is　beneficial　to　fault　diagnosis.　The　fusion　of　the　original　data　is　a　difficult　problem,　and　it　is　hard　to　express　effective　information　via　traditional　algorithms.　This　paper　presents　an　adaptive　data　fusion　strategy　based　on　deep　learning　called　the　convolutional　neural　network　with　atrous　convolution　for　the　adaptive　fusion　of　multiple　source　data　(FAC-CNN).　Specifically,　an　adaptive-sized　convolution　kernel　that　matches　the　channel　of　data　sources　is　constructed　to　capture　multi-source　data　without　tedious　preprocessing,　and　the　channel　of　data　sources　is　not　limited.　The　atrous　convolution　kernel　is　introduced　to　expand　the　field　of　view　of　the　FAC-CNN　and　extracts　fusion　sequence　features　without　repeated　computation,　resulting　in　improved　stability.　The　1D-CNN　is　added　to　extract　features　after　atrous　convolution.　In　addition,　batch　normalization　optimizes　the　distribution　of　fusion　data　and　the　structure　of　the　model.　The　parametric　rectified　linear　unit　activation　function　and　global　average　pooling　are　also　introduced　to　improve　network　performance.　The　proposed　method　is　validated　on　an　industrial　fan　system　with　non-manufacturing　faults　and　a　centrifugal　pump.　Compared　with　other　fusion　methods　and　diagnosis　algorithms　based　on　feature　engineering,　namely　CNN,　ANN,　and　SVM,　the　FAC-CNN　model　is　found　to　exhibit　superior　performance.　(c)　2020　Elsevier　Ltd.　All　rights　reserved.