@article{scholars9250,
       publisher = {Society of Chemical Engineers, Japan},
         journal = {Journal of Chemical Engineering of Japan},
           pages = {430--438},
            year = {2017},
           title = {Online anomaly detection of distillation tower system using adaptive resonance theory},
          volume = {50},
            note = {cited By 6},
          number = {6Speci},
             doi = {10.1252/jcej.16we360},
          author = {Hori, Y. and Yamamoto, H. and Suzuki, T. and Okitsu, J. and Nakamura, T. and Maeda, T. and Matsuo, T. and Bt Zabiri, H. and Tufa, L. D. and Marappagounder, R.},
            issn = {00219592},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021771504&doi=10.1252\%2fjcej.16we360&partnerID=40&md5=16b24700ec35c605987056f6ef0e5875},
        abstract = {An anomaly detection system based on adaptive response theory (ART) for industrial plants was proposed. The detection system has several ART applied to subsystems of the plant to narrow down the cause of the anomalies. An examination of online anomaly detection tests on whether or not the proposed system is applicable to a distillation tower system was performed. Four cases of anomaly operations, e.g., valve sticking and tray upset, that would cause quality or yield losses in the product were considered. Learning normal operation data revealed that the proposed system successfully detected the anomalies in all cases, and no false positives were noted in normal operation. Additionally, the system narrowed down the cause of the anomalies through using the results of each ART system, thus, demonstrating the applicability of the system for the distillation tower system.}
}