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

Alexander Nikitin, Samuel Kaski

Machine Learning and Robotics

Aalto University

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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
Subtitle of host publication	Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Place of Publication	New York, NY
Publisher	ACM Special Interest Group
Pages	3682-3690
Number of pages	9
ISBN (Electronic)	9781450393850
DOIs	https://doi.org/10.1145/3534678.3539196
Publication status	Published - 14 Aug 2022
Event	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Duration: 14 Aug 2022 → 18 Aug 2022

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Period	14/08/22 → 18/08/22

Keywords

applications
bayesian optimization
human-in-the-loop
machine learning
predictive maintenance

Access to Document

10.1145/3534678.3539196

Cite this

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 (pp. 3682-3690). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). ACM Special Interest Group. https://doi.org/10.1145/3534678.3539196

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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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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. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Special Interest Group, New York, NY, pp. 3682-3690, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 14/08/22. https://doi.org/10.1145/3534678.3539196

Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations. / Nikitin, Alexander; Kaski, Samuel.
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY: ACM Special Interest Group, 2022. p. 3682-3690 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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PY - 2022/8/14

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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.

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Nikitin A, Kaski S. 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. New York, NY: ACM Special Interest Group. 2022. p. 3682-3690. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3534678.3539196

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

Abstract

Publication series

Conference

Keywords

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Turing AI Fellowship: Human-AI Research Teams - Steering AI in Experimental Design and Decision-Making

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Turing AI Fellowship: Human-AI Research Teams - Steering AI in Experimental Design and Decision-Making

Cite this