Integrated Fault Tree and Case Analysis for Equipment Conventional Fault IETM Diagnosis

Area of Science:

• Engineering
• Computer Science
• Maintenance

Background:

• Routine equipment failures often stem from human error, tools, parts, or environmental factors.
• Conventional fault diagnosis methods are established but can be enhanced with digital technologies.
• Digital twins offer interactive diagnostic capabilities for equipment failures.

Purpose of the Study:

• To propose a digital twin-based model for efficient routine equipment fault diagnosis.
• To enhance the efficiency and interactivity of the fault diagnosis process.
• To integrate digital twin data with Interactive Electronic Technical Manuals (IETMs) for improved troubleshooting.

Main Methods:

• Developed a digital twin-based equipment routine fault diagnosis model.
• Designed a fault diagnosis scheme combining digital twin data and IETM interactivity.
• Converted equipment fault trees into an IETM fault data model (DM) stored in a database.
• Utilized fault tree analysis (FTA) with real-time twin data for diagnosis and guidance.
• Employed case-based reasoning to handle discrepancies between real-time data and the fault library.

Main Results:

• The proposed model enables rapid and interactive diagnosis of routine equipment failures.
• Integration with IETM provides step-by-step guidance for fault isolation and maintenance.
• The system effectively handles situations where real-time data partially matches existing fault information.
• A similarity threshold mechanism is used to identify and present relevant fault data models for diagnosis.

Conclusions:

• The digital twin and IETM integrated approach significantly improves routine equipment fault diagnosis efficiency and user interaction.
• This method provides a robust framework for real-time diagnostics, maintenance guidance, and handling data inconsistencies.
• The structured fault database and case analysis enhance the accuracy and responsiveness of the diagnostic system.