Explainable anomaly detection framework for predictive maintenance in manufacturing systems

Heejeong Choi, Donghwa Kim, Jounghee Kim, Jina Kim, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


To conduct preemptive essential maintenance, predictive maintenance detects the risk of unexpected shutdowns in a manufacturing system, thereby ensuring operational continuity. Traditional methods that heavily rely on the domain knowledge of expert engineers to detect any abnormal status in processing facilities are extremely time-consuming and domain-dependent. Conversely, recently studied data-driven approaches without much domain knowledge have yielded fairly good performance. However, most only identify whether the current status is normal or abnormal and do not offer any explanations or analyses. In this paper, we propose a real-time explainable anomaly detection framework for predictive maintenance in a manufacturing system. Various well-known anomaly detection algorithms are investigated to construct a framework suitable for shutdown prognosis. In addition, model interpretation techniques are also employed to provide a reasonable explanation for a detected shutdown. The experimental results on a real-world dataset derived from a chemical process show that the proposed framework could identify abnormal signs early and derive significant causes for each detected shutdown.

Original languageEnglish
Article number109147
JournalApplied Soft Computing
Publication statusPublished - 2022 Aug

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. NRF-2022R1A2C2005455 ). This work was also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00034 , Clustering technologies of fragmented data for time-based data analysis )

Publisher Copyright:
© 2022 Elsevier B.V.


  • Explainable anomaly detection
  • Isolation forest
  • Manufacturing system
  • Predictive maintenance
  • Shapley additive explanations

ASJC Scopus subject areas

  • Software


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