论文标题
人工大规模预测工作站
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
论文作者
论文摘要
预测维护（PDM）是基于对系统状况的统计分析安排维护操作的任务。我们提出了一种人类的PDM方法，其中机器学习系统可以预测工作站集（计算机，笔记本电脑和服务器）中的未来问题。我们的系统与领域专家进行互动，以改善预测并引起他们的知识。在我们的方法中，域专家不仅作为正确标签的提供者，就像在传统的主动学习中一样，还包括作为显式决策规则反馈的来源。该系统是自动化的，并且设计为易于扩展到新的领域，例如维护多个组织的工作站。此外，我们开发了一个模拟器，用于在受控环境中进行可复制实验，并在现实生活中的PDM大规模案例中部署系统，其中数千个工作站为数十家公司提供。
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.
