Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929421847363584 |
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| author | Trilla, Alexandre Rajendran, Rajesh Yiboe, Ossee Possamaï, Quentin Mijatovic, Nenad Vitrià, Jordi |
| author_facet | Trilla, Alexandre Rajendran, Rajesh Yiboe, Ossee Possamaï, Quentin Mijatovic, Nenad Vitrià, Jordi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11056 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept Trilla, Alexandre Rajendran, Rajesh Yiboe, Ossee Possamaï, Quentin Mijatovic, Nenad Vitrià, Jordi Machine Learning Artificial Intelligence Methodology 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. |
| title | Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2407.11056 |