In　wastewater　treatment　process　(WWTP),　abnormal　data　seriously　reduce　data　quality　rendering　the　application　techniques　impractical.　The　implementation　of　abnormal　data　detection　is　challenging　due　to　the　nonlinear　nature　of　WWTP.　Typically,　constructing　an　accurate　anomaly　detector　requires　large　amounts　of　labeled　data,　which　is　difficult　in　practice.　Thus,　a　self-supervised　memory　enhanced　deep　clustering　method　(SMEL)　is　proposed　to　detect　abnormal　data　without　using　any　labels.　First,　a　self-supervised　deep　clustering　network,　combining　stacked　autoencoders　and　the　clustering　algorithm,　leverages　unlabeled　data　to　extract　nonlinear　features　and　capture　the　normal　pattern.　Second,　an　adaptive　weight　objective　function,　jointly　optimizing　the　reconstruct　error　and　clustering　error,　is　designed　to　obtain　a　robust　clustering　structure.　Third,　double　memory　enhanced　modules,　consisting　of　a　centroid　memory　and　a　score　memory,　are　presented　to　enhance　training　stability　and　detection　accuracy.　Finally,　experiments　on　three　WWTP　datasets　show　that　SMEL　achieves　the　highest　detection　accuracy.　IEEE