This　article　is　to　provide　qualified　images　of　abnormal　combustion　state　for　the　research　of　machine　vision　in　municipal　solid　waste　incineration　(MSWI)　process.　Owing　to　the　scarcity　of　the　images　of　abnormal　combustion　state　and　the　high　cost　of　labeling,　it　is　difficult　to　obtain　sufficient　images　of　abnormal　combustion　state.　Aim　at　the　problem,　this　paper　proposes　a　method　for　generating　images　of　abnormal　combustion　state　based　on　a　deep　convolutional　generative　adversarial　network　(DCGAN).　First,　the　real　image　data　of　abnormal　combustion　state　is　preprocessed.　Second,　the　abnormal　combustion　state　image　generation　generates　false　combustion　images.　Third,　the　real　images　and　the　generated　images　are　fed　into　the　discrimination　network.　The　loss　values　are　used　to　train　the　discrimination　and　generation.　Finally,　whether　to　update　the　parameters　of　the　generation　and　discrimination　network　is　determined　by　the　error　and　epoch.　The　qualified　generated　abnormal　combustion　state　images　are　obtained　after　the　epoch　setting　met.　The　evaluation　result　of　the　generated　image　quality　based　on　the　Fréchet　Inception　Distance　(FID)　shows　that　DCGAN　can　realize　the　generation　of　abnormal　combustion　state　images.　©　2021　IEEE.