Using Functional Blocks for Rolling Stock Troubleshooting: Sequential Augmented Reality Assistant (SARA)

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Abstract
Maintenance and troubleshooting contribute to the overall economic efficiency of rolling stock throughout its lifecycle, yet these activities are resource-intensive and incur significant costs. In the era of Industry 4.0 implementation, the escalating complexity of rolling stock poses challenges in troubleshooting and maintenance decision-making. Recognizing this, there is a pressing need for a structured solution that empowers maintenance operators with tools for effective organization, analysis, and real-time presentation of rolling stock system failures, facilitating streamlined troubleshooting and maintenance guidance. Functional blocks, providing systematic structuring of processes, and Augmented Reality (AR), offering enhanced visualization and decision support, emerge as solutions to navigate and streamline these challenges in the evolving industrial landscape. The intricacies of existing fault-diagnosing systems, particularly interconnected transmission of functional blocks for troubleshooting and AR integration, lack detailed explanation, hindering effective solutions. To address these challenges, this work leverages AR for troubleshooting rolling stock failures, introducing functional blocks to (1) collect maintenance and fault data, (2) integrate maintenance information based on failure diagnostics and maintenance patterns, and (3) deliver this information to maintenance operators through AR. The developed Sequential Augmented Reality Assist (SARA) method, demonstrates the sequential implementation of AR troubleshooting functional blocks in an industrial context, providing a systematic solution to the complexities involved.
Funding Information
  • PPP Surcharge for Research and Innovation from the Dutch Ministry of Economic Affairs and Climate
  • Netherlands Railways
  • University of Twente

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