# End-Edge-Cloud Collaborative Monitoring System with an Intelligent Multi-Parameter Sensor for Impact Anomaly Detection in GIL Pipelines

## TL;DR

This paper introduces a smart monitoring system for detecting and locating structural impacts in gas-insulated transmission lines using a combination of sensors and machine learning.

## Contribution

The novel contribution is an end-edge-cloud system with a lightweight multi-parameter sensor and XGBoost model for impact anomaly detection in GIL pipelines.

## Key findings

- Fusing acceleration and angular velocity features enables reliable impact region discrimination with a single sensor.
- The XGBoost model achieves accurate impact location identification at the edge tier.
- The system offers low-cost and easily deployable structural health monitoring for GIL pipelines.

## Abstract

Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL monitoring approaches mainly emphasize electrical and gas-state parameters, while lightweight solutions capable of rapidly detecting and localizing impact-induced structural anomalies remain limited. To address this gap, this paper proposes an intelligent end-edge-cloud monitoring system for impact anomaly detection in GIL pipelines. Numerical simulations are first conducted to analyze the dynamic response characteristics of the pipeline under impacts of varying magnitudes, orientations, and locations, revealing the relationship between impact scenarios and vibration mode evolution. An end-tier multi-parameter intelligent sensor is then developed, integrating triaxial acceleration and angular velocity measurement with embedded lightweight computing. Laboratory impact experiments are performed to acquire sensor data, which are used to train and validate a multi-class extreme gradient boosting (XGBoost) model deployed at the edge tier for accurate impact-location identification. Results show that, even with a single sensor positioned at the pipeline midpoint, fusing acceleration and angular velocity features enables reliable discrimination of impact regions. Finally, a lightweight cloud platform is implemented for visualizing structural responses and environmental parameters with downsampled edge-side data. The proposed system achieves rapid sensor-level anomaly detection, precise edge-level localization, and unified cloud-level monitoring, offering a low-cost and easily deployable solution for GIL structural health assessment.