Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept

Alexandre Trilla, Rajesh Rajendran, Ossee Yiboe, Quentin Possamaï, Nenad Mijatovic +1 more
7/10/2024
cs.LGcs.AIstat.ME

No official code found. Best implementation: atrilla/ci4ts (1 star, last commit 2024-07-08).

Abstract

This paper describes the development of a counterfactual Root Cause Analysis diagnosis approach for an industrial multivariate time series environment. It drives the attention toward the Point of Incipient Failure, which is the moment in time when the anomalous behavior is first observed, and where the root cause is assumed to be found before the issue propagates. The paper presents the elementary but essential concepts of the solution and illustrates them experimentally on a simulated setting. Finally, it discusses avenues of improvement for the maturity of the causal technology to meet the robustness challenges of increasingly complex environments in the industry.

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Cite this paper

@article{trilla2024industrialgrade,
  title  = {Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept},
  author = {Alexandre Trilla and Rajesh Rajendran and Ossee Yiboe and Quentin Possamaï and Nenad Mijatovic and Jordi Vitrià},
  year   = {2024},
  eprint = {2407.11056},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.11056v1}
}

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