Integrating a Causal Foundation Model into a Prescriptive Maintenance Framework for Optimising Production-Line OEE

TL;DR

This paper introduces a causal foundation model integrated into prescriptive maintenance to better diagnose failures and recommend effective interventions, thereby optimizing production-line OEE.

Contribution

It presents a novel causal machine learning approach that enables active prescription and simulation of fixes, moving beyond traditional predictive models in manufacturing maintenance.

Findings

Causal model outperforms non-causal baselines in estimating intervention effects.

The approach effectively identifies root causes and operational impacts.

Simulation results suggest improved decision-making for maintenance actions.

Abstract

The transition to prescriptive maintenance (PsM) in manufacturing is critically constrained by a dependence on predictive models. Such purely predictive models tend to capture statistical associations in the data without identifying the underlying causal drivers of failure, which can lead to costly misdiagnoses and ineffective measures. 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. 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 to optimise KPIs such as Overall Equipment Effectiveness (OEE). For this purpose, a pre-trained causal foundation model is used as a ``what-if'' simulator to estimate the effects of potential fixes. By estimating the causal effect of each intervention on system-level KPIs, specific actions can be recommended for the production line. This can help identify plausible root causes and quantify their operational impact. The model is evaluated using semi-synthetic manufacturing data and compared with non-causal and causal baseline machine learning models. This paper provides a technical basis for a human-centred approach, allowing engineers to test potential solutions in a causal environment to make more effective operational decisions and reduce costly downtimes.