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Shao-Fan Lien1, Chun-Chieh Wang2, Juhng-Perng Su3

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A new Logistical Remote Association Repair Framework (LRARF) aids repairmen in maintaining large-scale systems. This system uses mobile devices and data mining for efficient module identification and repair, improving system availability.

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Area of Science:

  • Logistics and Maintenance Engineering
  • Computer Science and Artificial Intelligence

Background:

  • Large-scale system maintenance is crucial for logistics support planning.
  • Efficient identification and repair of faulty modules are challenging.
  • Existing methods may lack real-time support and automated diagnostics.

Purpose of the Study:

  • To develop a Logistical Remote Association Repair Framework (LRARF) for enhanced system availability.
  • To provide repairmen with tools for quick identification and maintenance of faulty modules.
  • To integrate smart mobile devices, database management, and remote support.

Main Methods:

  • Development of the Logistical Remote Association Repair Framework (LRARF) with four subsystems: smart mobile phones, Database Management System (DBMS), Maintenance Support Center (MSC), and wireless networks.
  • Utilizing smart mobile phones to capture QR-codes and images of faulty circuit boards.
  • Employing Linear Projective Transform (LPT) for QR-code calibration and an ANFIS-based data mining system for module identification and maintenance manual retrieval.

Main Results:

  • The proposed algorithm effectively recognizes invalid modules using QR-codes and image features.
  • The ANFIS-based system successfully identifies modules and retrieves corresponding maintenance manuals.
  • An Android-based platform demonstrated applicability in recognizing invalid modules, with live video support for non-recognizable components.

Conclusions:

  • The LRARF framework significantly aids repairmen in maintaining system availability.
  • The integration of mobile technology and data mining offers an efficient solution for complex system maintenance.
  • The system supports real-time diagnostics and provides recorded video for future analysis, enhancing overall maintenance effectiveness.