{"type":"ScholarlyArticle","title":"Plug-and-Play Unsupervised Fault Detection and Diagnosis for Complex Industrial Monitoring","authors":[{"name":"Maksim Golyadkin","slug":"maksim-golyadkin","given":"Maksim","family":"Golyadkin","affiliation":"HSE University; AIRI"},{"name":"Maria Shtark","slug":"maria-shtark","given":"Maria","family":"Shtark","affiliation":"AI Talent Hub , ITMO University"},{"name":"Petr Ivanov","slug":"petr-ivanov","given":"Petr","family":"Ivanov","affiliation":"AI Talent Hub , ITMO University"},{"name":"Alexander Kozhevnikov","slug":"alexander-kozhevnikov","given":"Alexander","family":"Kozhevnikov","affiliation":"AI Talent Hub , ITMO University"},{"name":"Leonid Zhukov","slug":"leonid-zhukov","given":"Leonid","family":"Zhukov","affiliation":""},{"name":"Ilya Makarov","slug":"ilya-makarov","given":"Ilya","family":"Makarov","affiliation":"AIRI; AI Talent Hub , ITMO University; ISP RAS Research Center for Trusted Artificial Intelligence"}],"abstract":"Today industrial facilities are equipped with lots of sensors throughout all the production line for monitoring means. Gathered data can be used to detect and predict failures; however, manual labeling of large amounts of data for supervised learning is complicated. This paper introduces an innovative approach to unsupervised fault detection and diagnosis tailored for monitoring industrial chemical processes. We showcase the efficacy of our model using two publicly accessible datasets from the Tennessee Eastman Process, each containing various faults. Furthermore, we illustrate that by fine-tuning the model on a limited amount of labeled data, it achieves performance close to that of a state-of-the-art model trained on the entire dataset.","doi":"10.24963/ijcai.2024/1005","url":"https://ijcai.org/proceedings/2024/1005","pdf":"https://ijcai.org/proceedings/2024/1005.pdf","markdown":"https://ijcai.org/proceedings/2024/1005.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1005","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1005,"pages":{"first":8669,"last":8673},"keywords":["Machine Learning: ML: Unsupervised learning","Data Mining: DM: Anomaly/outlier detection","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Time series and data streams","Machine Learning: ML: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Bi and Zhao, 2021] Xiaotian Bi and Jinsong Zhao. 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