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

Abstract

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
Volume125
DOIs
Publication statusPublished - 2022 Aug

Keywords

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

ASJC Scopus subject areas

  • Software

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