Machine　learning　models　have　been　widely　successful　in　the　field　of　intelligent　fault　diagnosis.　Most　of　the　existing　machine　learning　models　are　deployed　in　static　environments　and　rely　on　precollected　datasets　for　offline　training,　which　makes　it　impossible　to　update　the　models　further　once　they　are　established.　However,　in　the　open　and　dynamic　environment　in　reality,　there　is　always　incoming　data　in　the　form　of　streams,　including　new　categories　of　data　that　are　constantly　generated　over　time.　In　addition,　the　operating　conditions　of　mechanical　equipment　are　time-varying,　which　results　in　continuous　stream　data　that　are　nonindependently　and　homogeneously　distributed.　In　industrial　applications,　the　diagnosis　problem　of　nonindependent　and　identically　distributed　continuous　streaming　data　is　referred　to　as　the　cross-domain　class　incremental　diagnosis　problem.　To　address　the　cross-domain　class　incremental　problem,　a　novel　cross-domain　class　incremental　broad　network　(CDCIBN)　is　proposed.　Specifically,　to　solve　the　nonindependent　identically　distributed　problem,　a　novel　domain-adaptation　learning　loss　function　is　first　designed,　which　enables　the　conventional　broad　network　to　handle　the　category　increment　task　well.　Then,　a　cross-domain　class　incremental　learning　mechanism　is　designed,　which　learns　new　categories　while　retaining　the　knowledge　of　old　categories　well　enough　without　replaying　old　category　data.　The　effectiveness　of　the　proposed　method　is　evaluated　through　multiple　mechanical　failure　increment　cases.　Experimental　analysis　demonstrates　that　the　designed　CDCIBN　has　significant　advantages　in　the　variable　working　condition　class　incremental　application.　IEEE