RESEARCH PAPER
An Integrated Method for Predictive State Assessment and Path Planning for Inspection Robots in Island-Based Unmanned Substations
1
Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, China
2
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan university of technology, China
3
School of Electrical Engineering, Naval University of Engineering, China
Submission date: 2024-12-20
Final revision date: 2025-03-18
Acceptance date: 2025-04-13
Online publication date: 2025-04-17
Publication date: 2025-04-17
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):203994
HIGHLIGHTS
- A novel Conv2D-PSE-iTransformer improves multivariate time series predictions.
- Dynamic AHP adjusts temperature weights for transformer health assessment.
- DRL-based path planning prioritizes high-risk equipment with optimized routes.
- Simulated Annealing and Pruning enhance DRL efficiency and solution accuracy.
KEYWORDS
Deep Reinforcement LearningPredictive MaintenanceRobotic InspectionConv2D-PSE-iTransformerDynamic Analytic Hierarchy Process
TOPICS
ABSTRACT
To address the challenge of robotic inspection and maintenance in unmanned environments, this paper presents an integrated approach combining Conv2D-PSE-iTransformer for equipment state prediction, Dynamic Analytic Hierarchy Process (D-AHP) for health assessment, and deep reinforcement learning for optimized path planning. The Conv2D-PSE-iTransformer accurately predicts the operational state of electrical equipment, which serves as a critical input for the D-AHP evaluation. Based on the predicted state, D-AHP dynamically assesses the health of the equipment, enabling the identification of high-risk components that require immediate attention. Based on these evaluations, the DRL-based path planning generates optimized inspection routes that prioritize these high-risk areas while ensuring complete coverage with minimal inspection time. Experimental results demonstrate the effectiveness of this integrated method, highlighting its ability to reduce inspection time and enhance the overall efficiency, safety, and reliability of robotic inspections in complex, high-risk environments.
ACKNOWLEDGEMENTS
National Key R&D Program of China (Key Special Project for Marine Environmental Security and Sustainable Development of Coral Reefs 2022-3.1,NO: 2022YFC3102805)
Independent research and development project of Naval Engineering University: Identification of ship cabin equipment based on multispectral images
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