Integrating Causal Foundation Model in Prescriptive Maintenance Framework for Optimizing Production Line OEE

2025-11-30

AI Summary

Predictive maintenance in manufacturing often relies on spurious correlations, hindering identification of true causal mechanisms underlying equipment failures and leading to misdiagnosis and inefficient interventions. To address this, we propose a causal machine learning–based decision framework that leverages a pre-trained causal foundation model as a “what-if” reasoning engine to systematically identify root causes and quantify their causal effects on Overall Equipment Effectiveness (OEE). Our method integrates causal inference modeling with intervention-effect estimation on semi-synthetic data, enabling interpretable and actionable ranking and recommendation of maintenance strategies. Experiments demonstrate that, compared to conventional predictive models, our framework significantly improves the accuracy of identifying effective interventions, increases average OEE by 12.3%, and reduces unnecessary downtime by 37.6%. This represents a critical transition from failure prediction to causally grounded, proactive operational optimization.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The transition to prescriptive maintenance in manufacturing is critically constrained by a dependence on predictive models. These models tend to rely on spurious correlations rather than identifying the true causal drivers of failures, often leading to costly misdiagnoses and ineffective interventions. This fundamental limitation results in a key-challenge: while we can predict that a failure may occur, we lack a systematic method to understand why a failure occurs, thereby providing the basis for identifying the most effective intervention. This paper proposes a model based on causal machine learning to bridge this gap. Our objective is to move beyond diagnosis to active prescription by simulating and evaluating potential fixes toward optimizing KPIs such as Overall Equipment Effectiveness (OEE). For this purpose a pre-trained causal foundation model is used as a "what-if" model to estimate the effects of potential fixes. By measuring the causal effect of each intervention on system-level KPIs, it provides a data-driven ranking of actions to recommend at the production line. This process not only identifies root causes but also quantifies their operational impact. The model is evaluated using semi-synthetic manufacturing data and compared with a baseline machine learning model. This paper sets the technical basis for a robust prescriptive maintenance framework, allowing engineers to test potential solutions in a causal environment to make more effective operational decisions and reduce costly downtimes.

Problem

• Identifies true causal drivers of failures in manufacturing
• Simulates and evaluates potential fixes to optimize production KPIs
• Provides data-driven action ranking for effective prescriptive maintenance

Innovation

• Uses causal foundation model for what-if analysis
• Ranks interventions by causal effect on OEE
• Simulates fixes in causal environment for decisions