Predicting maintenance costs of an IT system using artificial intelligence models

Nathan Bosch, Emmanuel Okafor, Marco Vriens and Lambert Schomaker
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Nathan Bosch: Machine Learning Engineer, Lyft, Germany
Emmanuel Okafor: Postdoctoral Researcher, SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals, Saudi Arabia
Marco Vriens: CEO, Kwantum, USA
Lambert Schomaker: Professor in Artificial Intelligence, University of Groningen, The, The Netherlands

Applied Marketing Analytics: The Peer-Reviewed Journal, 2024, vol. 10, issue 1, 68-76

Abstract: Predictive maintenance is a maintenance policy where the goal is to detect potential future maintenance risks in a system so that the maintenance process can be optimised before system faults occur. This paper describes a deep learning model that does not require domain expertise. Deep learning approaches have several benefits over explicit statistical modelling: (1) they require far less domain-specific knowledge; (2) if the underlying data-generating mechanism of assets changes, a deep learning model would only need to be retrained to learn these new changes; (3) they can capture non-linear and complex multidimensional relationships; and (4) they may outperform rule-based or statistical methods. The paper describes how the model predicts maintenance-relevant events, along with the cost of the upcoming event and the time when it will happen. The paper describes the use of a long short-term memory architecture for our deep learning model. By doing so, the cost values represent a real, quantitative value of the potential maintenance costs in the future of an asset. Event, cost and time prediction are all achieved with high accuracy. This allows for the development of maintenance solutions without the initial high degree of domain process knowledge required. The artificial intelligence model can be used to raise an alarm when the cost values exceed some threshold, when the frequency of high-cost events increases significantly over the lifetime of an asset, or when the expected cost exceeds the cost of maintenance.

Keywords: predictive maintenance; deep learning; long short-term memory; LSTM; cost prediction; time prediction (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2024
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