Predictive Models for Maintenance Optimization: An Analytical Literature Survey of Industrial Maintenance Strategies

Merkt, Oana

doi:10.1007/978-3-030-43353-6_8

Cited by 4 publications

(1 citation statement)

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“…Moreover, this study does not propose a specific taxonomy for DM in PdM, it analyzes the proposals directly by the type of algorithm used, often distinguishing between traditional ML, also called shallow learning, and DL. Merkt (2020) reviews a selection of 19 works between 2016 and 2019 analyzing the most frequent ML techniques in CM, PvM, and PdM. It studied the pros and cons of implantation from an industrial perspective.…”

Section: Related Workmentioning

confidence: 99%

Data mining in predictive maintenance systems: A taxonomy and systematic review

Esteban

Zafra

Ventura

2022

WIREs Data Min & Knowl

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Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things

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Section: Related Workmentioning

confidence: 99%