Hybrid causal analysis combining a nonparametric multiplicative regression causality estimator with process connectivity information

Landman, Rinat; Jämsä‐Jounela, Sirkka‐Liisa

doi:10.1016/j.conengprac.2019.104140

Cited by 11 publications

(37 citation statements)

References 40 publications

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“…Application: Due to their ability to detect causal direction, causal discovery methods are especially useful in manufacturing applications of root cause analysis. Several approaches have focused on combining process topology and connectivity information to improve accuracy and reduce the computational load of root cause analysis in industrial board and board machine case studies [59][60][61][62][63]. Other studies demonstrated the application of causal discovery methods in the detection of disturbance propagation paths in fluid catalytic cracking units [64], mineral concentrator plants [65], and semiconductor production facilities [66].…”

Section: Root Cause Analysismentioning

confidence: 99%

“…Algorithmic improvements are focused on applications of principal component analysis [64], extension to multivariate scenarios [32], or improvements in the causality analysis models [33,69,71]. On the contrary, the latter approaches focus on the integration of Granger causality and transfer entropy approaches with process schematic data in adjacency (connectivity) matrix form [59][60][61][62][63]. Modern approaches, such as deep learning combined with graph processing techniques, have been applied in the context of root cause analysis in [66].…”

Section: Root Cause Analysismentioning

confidence: 99%

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Causal Discovery in Manufacturing: A Structured Literature Review

Vuković

Thalmann

2022

JMMP

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Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.

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Section: Root Cause Analysismentioning

confidence: 99%

Section: Root Cause Analysismentioning

confidence: 99%

Causal Discovery in Manufacturing: A Structured Literature Review

Vuković

Thalmann

2022

JMMP

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“…Non-parametric Multivariate Regression (NPMR) is another regression-based method that uses a non-parametric approach [25,26]. Consider a variable Y to be estimated from M predictors X m .…”

Section: Non-parametric Multivariate Regressionmentioning

confidence: 99%

From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods

Lucke

Chioua

Thornhill

2022

Journal of Process Control

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“…Causality has been widely used in fault detection and diagnosis (FDD) to understand the propagation path of abnormality 43–47 . In manufacturing systems where field knowledge is unavailable or insufficient, data‐driven causal discovery can help augment FDD efforts by identifying possible root causes and reducing alarm flooding 14,48–51 . Another common application of causal reasoning in manufacturing is to facilitate statistical models.…”

Section: Introductionmentioning

confidence: 99%

“…[43][44][45][46][47] In manufacturing systems where field knowledge is unavailable or insufficient, data-driven causal discovery can help augment FDD efforts by identifying possible root causes and reducing alarm flooding. 14,[48][49][50][51] Another common application of causal reasoning in manufacturing is to facilitate statistical models. Using causality, statistical machine learning models can learn more invariance and robust relationships and improve model explainability.…”

mentioning

confidence: 99%

Observational process data analytics using causal inference

Yang

Bequette

2022

AIChE Journal

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Voluminous process data are available with the paradigm shift toward smart manufacturing. However, most historical data are observational, containing noncausal correlations due to confounders and mediators. Estimating causal effects from observational data remains a bottleneck in leveraging them for active applications such as optimization and control. This work aims to introduce a causal modeling framework for analyzing observational process data and extracting quantitative causal information. We demonstrate a real-world application in steel manufacturing where causal inference is used to analyze observational production data and improve the steelmaking process.Additionally, we propose a novel formulation for identifying critical process parameters from observational data, where causal inference is combined with variance-based methods to estimate corresponding risks of interventions to the manufacturing system.The proposed methods are compared with statistical ones to illustrate that causally interpreting statistical correlation leads to problematic results, while the provided workflow generates satisfactory strategies for process improvement.

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Hybrid causal analysis combining a nonparametric multiplicative regression causality estimator with process connectivity information

Cited by 11 publications

References 40 publications

Causal Discovery in Manufacturing: A Structured Literature Review

Causal Discovery in Manufacturing: A Structured Literature Review

From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods

Observational process data analytics using causal inference

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