Optimized User Experience for Labeling Systems for Predictive Maintenance Applications

TL;DR

This paper develops a user-friendly graphical labeling interface for train maintenance systems, aiming to improve data annotation efficiency, reduce costs, and enhance predictive maintenance accuracy in rail infrastructure.

Contribution

It introduces a novel, usability-optimized labeling system tailored for predictive maintenance, integrating best practices in user interface design and validating its effectiveness.

Findings

System designed based on usability heuristics

Expected to improve annotation efficiency

Potential to reduce maintenance costs

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.