Fault　detection　in　the　waste　incineration　process　depends　on　high-temperature　image　observation　and　the　experience　of　field　maintenance　personnel,　which　is　inefficient　and　can　easily　cause　misjudgment　of　the　fault.　In　this　paper,　a　fault　detection　method　is　proposed　by　combining　stochastic　configuration　networks　(SCNs)　and　case-based　reasoning　(CBR).　First,　a　learning　pseudo　metric　method　based　on　SCNs　(SCN-LPM)　is　proposed　by　training　SCN　learning　models　using　a　training　sample　set　and　defined　pseudo-metric　criteria.　Then,　the　SCN-LPM　method　is　used　for　the　case　retrieval　stage　in　CBR　to　construct　the　fault　detection　model　based　on　SCN-CBR,　and　the　structure,　algorithmic　implementation,　and　algorithmic　steps　are　given.　Finally,　the　performance　is　tested　using　historical　data　of　the　MSW　incineration　process,　and　the　proposed　method　is　compared　with　typical　classification　methods,　such　as　a　Back　Propagation　(BP)　neural　network,　a　support　vector　machine,　and　so　on.　The　results　show　that　this　method　can　effectively　improve　the　accuracy　of　fault　detection　and　reduce　the　time　complexity　of　the　task　and　maintain　a　certain　application　value.