Method for Automatic Detection of the Technical State of Insulated Coolboxes in the Cold Chain Based on IoT Sensor Data

Maintaining temperature stability within the cold chain is critical to preventing food spoilage and financial loss. Insulated containers (coolboxes) using eutectic plates are widely employed to maintain passive cooling during transport. However, mechanical wear, seal damage, or improper latching can lead to microclimatic fluctuations inside the container, compromising product safety. This paper presents a non-invasive method for the automatic detection and classification of the technical state of coolboxes (fully functional, conditionally functional, and faulty) based on internal temperature and humidity measurements collected via wireless IoT sensors. Experimental trials conducted in a controlled climatic chamber simulating real-world operations demonstrate that micro-temperature gradients and heating rates allow for the early detection of seal degradation and incomplete door closures before critical thermal thresholds are violated. Specifically, the results confirm that uncompressed or damaged seals accelerate internal heating rates by up to 60%, and the diagnostic algorithm successfully classifies technical states with high sensitivity based on a degradation index threshold (∆Q ≤ 10% for fully functional, 10% < ∆Q ≤ 30% for conditionally functional, and ∆Q > 30% for faulty units). The proposed diagnostic framework supports predictive maintenance and improves overall cold-chain traceability.