Development of a Closed-Loop PLM Application for Vibration-Based Structural Health Monitoring of UAVs

Abstract

Highlights: What are the main findings? A closed-loop PLM framework was developed and experimentally validated for UAV health monitoring, integrating vibration-based SHM data with maintenance workflows in Aras Innovator. A structured UAV repository was established to link physical components, diagnostic analytics, and lifecycle records, enabling an automated fault response and traceable maintenance actions. What are the implications of the main findings? This study demonstrates how PLM can function as an active, data-driven environment for UAV reliability, safety, and predictive maintenance. The framework provides a scalable basis for integrating multi-UAV operations, real-time diagnostics, and digital-twin-based lifecycle management. Unmanned Aerial Vehicles (UAVs), particularly multirotor drones, require rigorous structural monitoring to ensure safe and reliable operation. Visual inspections are often inefficient and may miss early signs of damage. Even when faults are detected visually, effective repair requires contextual knowledge such as past repairs, part specifications, and supplier information. This study presents an implemented and experimentally validated closed-loop Product Lifecycle Management (PLM) system that integrates vibration-based structural health monitoring (SHM) with UAV maintenance workflows. A physical quadcopter platform is utilized to collect vibration data for training and testing under eight physically induced single-fault scenarios, including damaged propellers and loosened components. Deep learning models are trained on time-domain vibration data collected from onboard sensors to learn fault patterns and are then deployed in the proposed system for real-time fault classification. The GRU (Gated Recurrent Unit) model is selected for deployment due to its superior performance and lower computational cost and is integrated with a custom-developed UAV data repository within the Aras Innovator PLM platform. Experimental validation shows that the GRU model achieves 99.26% classification accuracy and a macro F1-score of 0.9917, confirming the reliability of the vibration-based fault detection approach. This end-to-end integration enables not only real-time fault detection but also lifecycle traceability, digital documentation, and data-driven maintenance decisions. Experimental validation across test runs confirms that the proposed system accurately detects structural faults and enables automated safety protocols and maintenance workflows. The system improves inspection efficiency and demonstrates how closed-loop PLM can move beyond static documentation to actively monitor, diagnose, and manage UAV health throughout its operational lifecycle.