For a client that provides smart temperature monitoring services

The Problem

Monitoring and ensuring appropriate temperatures of stocks such as food items is critical. Variations in temperature can end up damaging the stock. One of our clients who provide smart temperature monitoring services, wanted a system for cold storage areas and refrigerators that could automatically raise alarms if the temperature is different than what it should be. This is called anomaly detection.

Our Solution

Temperatures in cold storage areas often follow a frosting-defrosting cycle (see figure below) which can vary across different refrigerators. Furthermore, temperature cycles also depend on the environment. So simply raising an alarm if the temperature went beyond a certain range of value would have either raised too many false alarms or have missed out on a lot of different anomalies. Instead, we required a dynamic system that can adapt to different refrigerator cycles and environmental changes.

To address these challenges, we built a machine learning-based time series forecasting model that predicts normal temperature changes ahead in time. These predictions are dependent on the unique temperature cycles of a refrigerator or storage area and the environment around it. Once we have an accurate forecasting model, then we can simply raise an alarm if the actual temperature deviates from the forecast.

Results

Our AI system was able to automatically raise alarms whenever temperatures were anomalous thereby reducing chances of spoilage and ensuring freshness of stock. Automating this process also eliminated the need for continuous manual supervision. Some of the results are shown in the graphs below.