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PriMa-Causa: Causal Prescriptive Maintenance — arXiv:2610.31147

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.31147
License (as archived): CC0-1.0
Archived: 2026-10-23T15:15:19.818Z
SHA-256 of the archived text: 4090d07112cd0ecf4d3f1e6e05a67d09210c4bf6044cb1c511de5c11a09157a1
Stable URL of this page: https://metraipolis.com/documento/dc_283cb6ec8ea1

How to cite

PriMa-Causa: Causal Prescriptive Maintenance — arXiv:2610.31147 — arXiv (en), https://arxiv.org/abs/2610.31147 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_283cb6ec8ea1 (Metraipolis archive dc_283cb6ec8ea1, sha256 4090d07112cd0ecf4d3f1e6e05a67d09210c4bf6044cb1c511de5c11a09157a1)

BibTeX

@misc{dc283cb6ec8ea1,
  title = {PriMa-Causa: Causal Prescriptive Maintenance — arXiv:2610.31147},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.31147},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_283cb6ec8ea1; sha256 4090d07112cd0ecf4d3f1e6e05a67d09210c4bf6044cb1c511de5c11a09157a1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.31147. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automated manufacturing requires data-driven decisions under uncertainty and sparse observational records, and unplanned downtime is a dominant cost of getting them wrong. Prescriptive maintenance recommends a maintenance action against an impending failure rather than merely predicting it. Established reference architectures couple predictive models with rule- or optimisation-based recommendation logic. Selecting a corrective action, however, requires an interventional quantity: how the line behaviour would change if the action were set. Manufacturing data is observational, so a model fitted to predict the outcome estimates an associational quantity and may misdirect the choice of action without the prediction error revealing it. Causal estimators target the interventional quantity but require an expert-led causal study per case, for which the bounded decision window of operation leaves no room. This dissertation analyses how causal reasoning can be embedded into prescriptive maintenance so that actions are ranked by their estimated interventional effect. The result is PriMa-Causa, built from three components: a causal reasoning layer that integrates the estimate into the PriMa reference model, a parametric simulator that supplies the interventional ground truth absent from manufacturing data, and a graph-conditioned tabular causal foundation model that, pre-trained once on this synthetic production-line prior, answers interventional queries in a single forward pass. PriMa-Causa attains a lower prescription error than associational baselines, with the largest margin under weak overlap and sparse context, and a partially disclosed causal graph reduces this error further. The causal inductive bias of the synthetic prior and the graph conditioning can thus replace per-case estimator fitting, a step toward automating causal inference for autonomous and self-healing maintenance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.31147 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-23T15:09:08.588Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).