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

arXiv: https://arxiv.org/abs/2206.11574 · HTML: https://mlsanity.com/paper/2206.11574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [The Human Kernel](https://mlsanity.com/paper/1510.07389/) (2015)
- [Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI](https://mlsanity.com/paper/2005.13275/) (2020)
- [Decision Rule Elicitation for Domain Adaptation](https://mlsanity.com/paper/2102.11539/) (2021)

## Built on by (most cited first, 3 of 3)

- [TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series](https://mlsanity.com/paper/2305.11567/) (2023)
- [FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes](https://mlsanity.com/paper/2506.03278/) (2025)
- [Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies](https://mlsanity.com/paper/2603.15158/) (2026)

## Abstract (arXiv)

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.