A Three‐Stage Causal Root‐Cause Diagnostic Protocol for Nonstationary Industrial Time Series Data.
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| Title: | A Three‐Stage Causal Root‐Cause Diagnostic Protocol for Nonstationary Industrial Time Series Data. |
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| Authors: | Yalim, Cansu1 (AUTHOR) cyali001@odu.edu, Unal, Resit1 (AUTHOR), Handley, Holly A. H.1 (AUTHOR), Cuccureddu, Floriano1 (AUTHOR) fcuccuredd@wiley.com |
| Source: | International Journal of Intelligent Systems. 5/31/2026, Vol. 2026, p1-38. 38p. |
| Subjects: | Time-varying systems, Root cause analysis, Bayesian analysis, Process control systems, Condition-based maintenance, Change-point problems |
| Abstract: | Predictive maintenance (PdM) systems effectively forecast failures, but they often fail to find root causes, particularly when system dynamics change over time. This limitation arises from applying static causal models or decoupled segmentation to handle nonstationary industrial time series. For regime‐aware causal diagnostics and interventional effect estimation, we introduce a three‐stage time‐varying dynamic Bayesian network (TV‐DBN) protocol. Using a minimum description length (MDL) objective that connects segmentation to mechanism changes, Stage I jointly infers change points and regime‐specific graph structure. Stage II produces a completed partially directed acyclic graph (DAG) by orienting edges within each regime using a combination of score‐based search and conditional independence testing with false‐discovery‐rate control. Stage III uses MDL‐based weights to average over DAG extensions, truncated factorization to calculate h‐step interventional effects, and ancestral support gating to prevent risky extrapolation. The protocol localizes causal regime changes, generates more plausible regime‐specific structures than static baselines, and generates regime‐specific effect estimates that are consistent with known fault mechanisms, according to experiments conducted on the Tennessee Eastman process benchmark. In nonstationary industrial settings, the suggested "auditable contract" of assumptions supports more dependable root‐cause diagnosis and maintenance choices by giving practitioners evident standards for verifying causal claims. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194204748 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Three‐Stage Causal Root‐Cause Diagnostic Protocol for Nonstationary Industrial Time Series Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yalim%2C+Cansu%22">Yalim, Cansu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cyali001@odu.edu</i><br /><searchLink fieldCode="AR" term="%22Unal%2C+Resit%22">Unal, Resit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Handley%2C+Holly+A%2E+H%2E%22">Handley, Holly A. H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cuccureddu%2C+Floriano%22">Cuccureddu, Floriano</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fcuccuredd@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 5/31/2026, Vol. 2026, p1-38. 38p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Time-varying+systems%22">Time-varying systems</searchLink><br /><searchLink fieldCode="DE" term="%22Root+cause+analysis%22">Root cause analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Process+control+systems%22">Process control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Predictive maintenance (PdM) systems effectively forecast failures, but they often fail to find root causes, particularly when system dynamics change over time. This limitation arises from applying static causal models or decoupled segmentation to handle nonstationary industrial time series. For regime‐aware causal diagnostics and interventional effect estimation, we introduce a three‐stage time‐varying dynamic Bayesian network (TV‐DBN) protocol. Using a minimum description length (MDL) objective that connects segmentation to mechanism changes, Stage I jointly infers change points and regime‐specific graph structure. Stage II produces a completed partially directed acyclic graph (DAG) by orienting edges within each regime using a combination of score‐based search and conditional independence testing with false‐discovery‐rate control. Stage III uses MDL‐based weights to average over DAG extensions, truncated factorization to calculate h‐step interventional effects, and ancestral support gating to prevent risky extrapolation. The protocol localizes causal regime changes, generates more plausible regime‐specific structures than static baselines, and generates regime‐specific effect estimates that are consistent with known fault mechanisms, according to experiments conducted on the Tennessee Eastman process benchmark. In nonstationary industrial settings, the suggested "auditable contract" of assumptions supports more dependable root‐cause diagnosis and maintenance choices by giving practitioners evident standards for verifying causal claims. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/int/3436744 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 1 Subjects: – SubjectFull: Time-varying systems Type: general – SubjectFull: Root cause analysis Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Process control systems Type: general – SubjectFull: Condition-based maintenance Type: general – SubjectFull: Change-point problems Type: general Titles: – TitleFull: A Three‐Stage Causal Root‐Cause Diagnostic Protocol for Nonstationary Industrial Time Series Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yalim, Cansu – PersonEntity: Name: NameFull: Unal, Resit – PersonEntity: Name: NameFull: Handley, Holly A. H. – PersonEntity: Name: NameFull: Cuccureddu, Floriano IsPartOfRelationships: – BibEntity: Dates: – D: 31 M: 05 Text: 5/31/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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