Edge AI - Enabled Smart Sensors for Predictive Maintenance of Marine Vessels
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Abstract
Traditional Predictive Maintenance deployment on marine vessels requires continuous acquisition and transmission of large volumes of sensor data, resulting in high complexity, cost, and cybersecurity exposure that limit large-scale adoption.
This work presents RSL Smart Sensors, an Edge AI - enabled sensing platform that performs distributed on-sensor intelligence to significantly reduce data transmission, operational costs, and cyber risks. Sensor data are processed locally, with only health indicators and diagnostic insights transmitted to the cloud for decision support.
A novel machine learning algorithm is introduced to estimate machine operating conditions directly from vibration data under strict power constraints, without access to external operating parameters. Validation on marine auxiliary generator turbochargers demonstrates high accuracy and confirms the feasibility of high-performance predictive maintenance at the edge under limited power and information conditions.
How to Cite
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Predictive Maintenance, Edge AI, Edge Computing, Maritime, Cargo Vessels, Smart Sensors, Prognostics and Health Management
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