Fault　detection　and　diagnosis　(FDD)　have　been　major　concerns　in　abnormal　event　management　of　chemical　processes　for　decades.　Frequency-wise　variations　in　chemical　processes　are　not　considered　in　most　traditional　methods,　which　affects　the　monitoring　performance.　An　amplitude-frequency　images-based　convolutional　neural　network　(ConvNet)　is　proposed　for　FDD　in　chemical　processes.　The　fast　Fourier　transform　(FFT)　is　first　performed　on　data　slice　collected　within　a　period　to　extract　both　amplitude-wise　dynamics　and　frequency-wise　variations,　with　the　results　in　images.　Then,　the　amplitude-frequency　images　are　fed　into　ConvNet　for　FDD.　ConvNet　is　applied　as　a　binary　classifier,　in　which　each　classifier　corresponds　to　only　one　fault.　Thus,　an　expandable　framework　is　provided　to　incorporate　a　new　fault.　The　performance　of　the　proposed　amplitude-frequency　images-based　ConvNet　in　FDD　is　demonstrated　in　a　numerical　case　and　the　Tennessee　Eastman　process.