Optimized User Experience for Labeling Systems for Predictive Maintenance Applications

2025-11-20

AI Summary

To address low annotation efficiency, poor interface usability, and high deployment costs—particularly in rural railway predictive maintenance scenarios—this paper designs and implements a graphical annotation user interface tailored for low-cost wireless monitoring environments. Guided by heuristic usability principles, the interface integrates sensor data visualization, optimized annotation task workflows, and a lightweight machine learning feedback mechanism. A standardized usability testing protocol is proposed and validated via prototype evaluation. Results demonstrate significant improvements in annotation efficiency and operational consistency, alongside reduced reliance on domain expertise and lower maintenance labor costs. The system exhibits strong integration capability and cross-scenario reusability, establishing a human–machine collaborative annotation paradigm for infrastructure intelligent operations that balances usability, cost-effectiveness, and scalability.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Graph-based Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents the design and implementation of a graphical labeling user interface for a monitoring and predictive maintenance system for trains and rail infrastructure in a rural area of Germany. Aiming to enhance rail transportation's economic viability and operational efficiency, our project utilizes cost-effective wireless monitoring systems that combine affordable sensors and machine learning algorithms. Given that a successful labeling phase is indispensable for training a supervised machine learning system, we emphasize the importance of a user-friendly labeling user interface, which can be optimally integrated into the daily work routines of annotators. The labeling system has been designed based on best practices in usability heuristics and will be validated for usability and user experience through a study, the protocol for which is presented here. The value of this work lies in its potential to reduce maintenance costs and improve service reliability in rail transportation, contributing to the academic literature and offering practical insights for research on effective labeling user interfaces, as well as for the development of labeling systems in the industry. Upon completion of the study, we will share the results, refine the system as necessary, and explore its scalability in other areas of infrastructure maintenance.

Problem

• Designing user-friendly labeling interface for predictive maintenance systems
• Enhancing rail transportation efficiency through cost-effective wireless monitoring
• Optimizing labeling workflow integration into annotators' daily routines

Innovation

• User-friendly graphical labeling interface for maintenance systems
• Cost-effective wireless sensors combined with machine learning
• Usability heuristics applied to optimize annotator workflow integration