In　industrial　applications,　different　abnormal　conditions　of　a　machine　or　a　machinery　fleet　typically　occur　sequentially　instead　of　becoming　initially　known.　Therefore,　one　of　the　most　challenging　tasks　for　the　machinery　fault　diagnosis　is　to　incrementally　classify　unknown　abnormal　patterns.　For　this　reason,　an　incremental　novelty　identification　(INI)　method　is　proposed,　on　the　strength　of　deep　learning-based　representation　learning,　novelty　detection　and　incremental　learning.　In　the　INI　framework,　a　convolutional　neural　network　autoencoder　is　first　developed　for　high-level　feature　extraction　from　multiple　monitoring　signals.　An　online　sequential-extreme　learning　machine　(OS-ELM)　is　then　proposed　to　learn　healthy　features　of　the　normal　condition　initially　known.　The　newly　collected　data　are　employed　for　updating　the　OS-ELM　with　the　quantification　of　the　model　modification　for　novelty　detection.　An　incremental　learning　procedure　is　implemented　by　adding　a　new　OS-ELM　model　representing　the　novel　abnormal　pattern.　The　repository　of　OS-ELMs,　each　of　which　represents　a　specific　condition,　is　employed　for　incrementally　diagnosing　known　abnormal　patterns　with　the　identification　of　unknown　ones.　The　performance　of　the　proposed　INI　is　validated　under　experimental　scenarios　considering　both　a　benchmark　bearing　case　study　and　a　delta　three-dimensional　printer,　and　the　results　are　compared　with　other　state-of-the-art　approaches.