Imagine if your home AC could tell you exactly why it's losing efficiency, even if three different things are wrong at once, without needing a technician to visit. Instead of expensive sensors, it uses a digital twin—a virtual copy of the machine—to spot leaks or dirt buildup. It's like having a master mechanic living inside the software of your heater.
Small-scale cooling systems lack affordable tools to diagnose multiple simultaneous faults. This leads to wasted energy, higher maintenance costs, and shorter equipment lifespans.
A digital twin-based FDDE methodology and a synthetic database for various machines to validate fault detection and evaluation.
If you are a manufacturer dealing with high warranty costs and inefficient units in the field — this project developed a digital twin FDDE method that identifies up to 3 concurrent soft-faults. This allows you to offer a premium predictive maintenance feature using only cheap extra measurements.
If you are an energy service provider dealing with fluctuating client efficiency and high site-visit costs — this project developed a scalable monitoring tool that provides real-time estimation of performance indicators. This enables remote diagnosis of refrigerant charge and fouling without expensive hardware.
If you are a building operator dealing with unexpected cooling failures and high electricity bills — this project developed a decision-support service that evaluates fault severity. This helps you prioritize maintenance to extend system lifespan and reduce energy consumption.
Based on available project data, the method is designed to be affordable for small-scale systems, requiring only 'cheap extra measurements' and standard IoT devices rather than expensive industrial sensors.
The project specifically targets small- and medium-scale systems, as current solutions already exist for large industrial installations. It aims to bring that level of diagnostic power to smaller vapor compression systems.
Based on available project data, the project mentions current patents limit existing solutions to one fault at a time, while BEYOND aims to move beyond this state-of-the-art. Specific licensing terms are not provided.
The method preserves the original hardware of the vapor compression system, adding only a few low-cost sensors and utilizing ICT infrastructures and IoT devices for data processing.
The project period runs from 2024-09-01 to 2028-08-31, with the first year focused on establishing scientific and experimental bases for the digital twin.
The consortium is purely academic, consisting of 2 universities from Italy (Naples Federico II) and Spain (Polytechnic University of Valencia). With an industry ratio of 0%, the project is currently focused on high-level research and proof-of-concept rather than immediate commercial deployment.
Contact Universita degli Studi di Napoli Federico II
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