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A Holistic End-to-End Prescriptive Maintenance Framework

  • Original Paper
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Data-Enabled Discovery and Applications

Abstract

The concept of equipment maintenance is older than the industrial revolution. The mode, medium, and timing of maintenance during equipment life cycle have evolved from reactive maintenance to predictive maintenance to prescriptive maintenance. Prescriptive maintenance, which incorporates the Internet of Things, digitization, and artificial intelligence, has the potential to greatly improve upon proactive maintenance. The growth in applications based on prescriptive maintenance has been exponential. However existing solutions are piecemeal and lack complete solutions to keep equipment operating at optimal cost. We propose a Holistic end-to-end Prescriptive Maintenance Framework (HeePMF) that uses maintenance needs analysis, equipment, and operational data with predictive technologies and feedback to generate actionable insights. Other features implemented are personnel scheduling, supply chain improvement, field-replaceable unit (FRU) management, process improvement, and knowledge management. The working of framework (HeePMF) is demonstrated using datasets from the 2019 HACKtheMACHINE Data Science Competition. The implementation demonstrates data integration, selection of few critical discriminants using feature reduction, missing dataset computation, and noise removal. It ulilizes and demonstrates predictive algorithms to determine sub-components for impending failures at individual equipment and fleet levels, and then providing mechanism for complete repair solutions (FRUs order, service personnel scheduling, equipment downtime management). Steps like service personnel optimal deployment are implemented using a simulated dataset. This HeePMF is extensible and makes it truly prescriptive, resulting in a much reduced unplanned equipment downtime at optimal cost. This paper successfully defines extensible end-to-end holistic prescriptive equipment maintenance framework with optimal cost and demonstrates with a very thorough case study.

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Data Availability

The data was available at https://www.hackthemachine.ai/track2 when experiments were conducted during the month of February 2020 .

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Acknowledgments

We would like to thank the anonymous reviewers for the comments and suggestions.

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The code is not available.

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This work was conducted as part of the authors’ normal duties at the University of South Alabama.

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Authors and Affiliations

  1. School of Computing, University of South Alabama, Mobile, AL, USA

    Suresh Choubey, Ryan G. Benton & Tom Johnsten

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  1. Suresh Choubey
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Correspondence to Suresh Choubey.

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This article belongs to the Topical Collection: Data-Enabled Discovery for Industrial Cyber-Physical Systems

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Choubey, S., Benton, R.G. & Johnsten, T. A Holistic End-to-End Prescriptive Maintenance Framework. Data-Enabled Discov. Appl. 4, 11 (2020). https://doi.org/10.1007/s41688-020-00045-z

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