Optimizing the preventive maintenance frequency with causal machine learning
Optimizing the preventive maintenance frequency with causal machine learning
Maintenance is a challenging operational problem where the goal is to plan sufficient preventive maintenance (PM) to avoid asset overhauls and failures. Existing work typically relies on strong assumptions (1) to model the asset’s overhaul and failure rate, assuming a stochastic process with known hazard rate, (2) to model the effect of PM on this hazard rate, assuming the effect is deterministic or governed by a known probability distribution, and (3) by not taking asset-specific characteristics into account, but assuming homogeneous hazard rates and PM effects. Instead of relying on these assumptions to model the problem, this work uses causal inference to learn the effect of the PM frequency on the overhaul and failure rate, conditional on the asset’s characteristics, from observational data. Based on these learned outcomes, we can optimize each asset’s PM frequency to minimize the combined cost of failures, overhauls, and preventive maintenance. We validate our approach on real-life data of more than 4000 maintenance contracts from an industrial partner. Empirical results on semi-synthetic data show that our methodology based on causal machine learning results in individualized maintenance schedules that are more accurate and cost-effective than a non-causal approach that does not deal with selection bias and a non-individualized approach that prescribes the same PM frequency to all machines.
Causal inference, Individual treatment effects, Machine learning, Maintenance
Vanderscheuren, T.
5680395d-7c3a-4a8e-9e05-d563fbf0a4aa
Boute, R.
322624d4-6b5b-43a2-a007-17a834a979fd
Verdonck, Tim
8558b8f8-d412-4fb9-9784-9aba1d7323b6
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
April 2023
Vanderscheuren, T.
5680395d-7c3a-4a8e-9e05-d563fbf0a4aa
Boute, R.
322624d4-6b5b-43a2-a007-17a834a979fd
Verdonck, Tim
8558b8f8-d412-4fb9-9784-9aba1d7323b6
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Vanderscheuren, T., Boute, R., Verdonck, Tim, Baesens, Bart and Verbeke, Wouter
(2023)
Optimizing the preventive maintenance frequency with causal machine learning.
International Journal of Production Economics, 258, [108798].
(doi:10.1016/j.ijpe.2023.108798).
Abstract
Maintenance is a challenging operational problem where the goal is to plan sufficient preventive maintenance (PM) to avoid asset overhauls and failures. Existing work typically relies on strong assumptions (1) to model the asset’s overhaul and failure rate, assuming a stochastic process with known hazard rate, (2) to model the effect of PM on this hazard rate, assuming the effect is deterministic or governed by a known probability distribution, and (3) by not taking asset-specific characteristics into account, but assuming homogeneous hazard rates and PM effects. Instead of relying on these assumptions to model the problem, this work uses causal inference to learn the effect of the PM frequency on the overhaul and failure rate, conditional on the asset’s characteristics, from observational data. Based on these learned outcomes, we can optimize each asset’s PM frequency to minimize the combined cost of failures, overhauls, and preventive maintenance. We validate our approach on real-life data of more than 4000 maintenance contracts from an industrial partner. Empirical results on semi-synthetic data show that our methodology based on causal machine learning results in individualized maintenance schedules that are more accurate and cost-effective than a non-causal approach that does not deal with selection bias and a non-individualized approach that prescribes the same PM frequency to all machines.
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Accepted/In Press date: 31 January 2023
e-pub ahead of print date: 3 February 2023
Published date: April 2023
Additional Information:
Funding Information:
This work was supported by the BNP Paribas Fortis Chair in Fraud Analytics, BASF Chair on Robust Predictive Analytics, FWO Research Project G015020N , and FWO PhD Fellowship 11I7322N . The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government – department EWI.
Publisher Copyright:
© 2023 Elsevier B.V.
Keywords:
Causal inference, Individual treatment effects, Machine learning, Maintenance
Identifiers
Local EPrints ID: 476640
URI: http://eprints.soton.ac.uk/id/eprint/476640
ISSN: 0925-5273
PURE UUID: 29ca62ad-3344-4f4e-b59b-335b0bbe08dc
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Date deposited: 10 May 2023 17:00
Last modified: 31 Jul 2024 04:02
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Contributors
Author:
T. Vanderscheuren
Author:
R. Boute
Author:
Tim Verdonck
Author:
Wouter Verbeke
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