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

Autor:	Trilla, Alexandre, Rajendran, Rajesh, Yiboe, Ossee, Possamaï, Quentin, Mijatovic, Nenad, Vitrià, Jordi
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted for the Causal Inference for Time Series Data Workshop at the 40th Conference on Uncertainty in Artificial Intelligence (CI4TS 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.11056 Zobrazit plný text záznamu View this record from Arxiv