Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

Alexander Nikitin; Samuel Kaski

doi:10.1145/3534678.3539196

Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

^*Corresponding author for this work

University of Manchester

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

Abstract

Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.

Original language	English
Title of host publication	KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	ACM
Pages	3682–3690
ISBN (Electronic)	978-1-4503-9385-0
DOIs	https://doi.org/10.1145/3534678.3539196
Publication status	Published - 14 Aug 2022
MoE publication type	A4 Conference publication
Event	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Washington, United States Duration: 14 Aug 2022 → 18 Aug 2022 Conference number: 28 https://kdd.org/kdd2022/

Conference

Conference	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated title	KDD
Country/Territory	United States
City	Washington
Period	14/08/2022 → 18/08/2022
Internet address	https://kdd.org/kdd2022/

Keywords

predictive maintenance
machine learning
Bayesian optimisation
data mining
human-in-the-loop machine learning

Access to Document

10.1145/3534678.3539196

https://arxiv.org/abs/2206.11574

Cite this

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title = "Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations",

abstract = "Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.",

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author = "Alexander Nikitin and Samuel Kaski",

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doi = "10.1145/3534678.3539196",

language = "English",

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booktitle = "KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",

publisher = "ACM",

address = "United States",

note = "ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ; Conference date: 14-08-2022 Through 18-08-2022",

url = "https://kdd.org/kdd2022/",

}

Nikitin, A & Kaski, S 2022, Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations. in KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, pp. 3682–3690, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, District of Columbia, United States, 14/08/2022. https://doi.org/10.1145/3534678.3539196

TY - GEN

T1 - Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

AU - Nikitin, Alexander

AU - Kaski, Samuel

N1 - Conference code: 28

PY - 2022/8/14

Y1 - 2022/8/14

N2 - Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.

AB - Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.

KW - predictive maintenance

KW - machine learning

KW - Bayesian optimisation

KW - data mining

KW - human-in-the-loop machine learning

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EP - 3690

BT - KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

PB - ACM

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Y2 - 14 August 2022 through 18 August 2022

ER -

Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

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Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

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Conference

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Science-IT

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