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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 provides 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 process is called anomaly detection.
Simple monitoring systems often suffer from too many false alarms because temperatures in cold storage areas follow a complex frosting-defrosting cycle which varies across different refrigerators and depends on the environment. Simply raising an alarm if the temperature went beyond a certain static range would have either missed anomalies or raised too many false alarms.
To address these challenges, we built a custom time series forecasting model that predicts normal temperature changes ahead of time. These predictions are dependent on the unique temperature cycles of a refrigerator or storage area and the environment around it.
The predictive model constantly learns the normal behavior of the system. Once we have an accurate forecasting model, we can simply raise an alarm only when the actual temperature deviates significantly from the forecasted normal range.
This dynamic system adapts to varying cycles and environmental changes, effectively eliminating false alarms caused by predictable defrosting cycles.
Our AI system was able to automatically raise alarms whenever temperatures were anomalous, significantly reducing chances of spoilage and ensuring freshness of stock. Automating this process also eliminated the need for continuous manual supervision, achieving full predictive maintenance and ensuring maximum operational integrity.
Searching for AI development company which can resolve your cold storage detection problem? Yes, you have reached the right spot where you can find the solution to your problems. Then, why you are missing free AI consulting services at AxcelerateAI.
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