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

TL;DR

This paper introduces a human-in-the-loop machine learning system for large-scale predictive maintenance of workstations, integrating expert feedback to enhance prediction accuracy and adaptability across multiple organizations.

Contribution

It presents a novel approach combining human expertise with machine learning for predictive maintenance, including a simulator for controlled experiments and deployment in real-world settings.

Findings

System improves prediction accuracy with expert feedback

Successfully deployed at large scale across multiple organizations

Provides a flexible, extendable framework for various domains

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.