Automation has been used within warehouses for many years, however, it really did become essential during the pandemic as e-commerce sales grew and the number of personnel on-site was limited.
The pandemic has reinforced why it’s critical to be continuously monitoring automation assets on-site to ensure they are running to optimum performance. This includes the monitoring of vital components such as the belts and drives on conveyor and sortation systems. Failure to do so can see breakdowns forcing the whole system to shut down, leading to missed deadlines and big delays.
Temperature monitoring for conveyor maintenance and servicing
Previously, maintenance engineers used handheld thermal imaging cameras to monitor the temperature of belts and drives. As the bearings start to wear they create extra heat through vibration. And when conveyor belts stretch or are incorrectly mounted on the drives, they get warmer than usual. By monitoring this temperature rise, engineers can identify if systems are operating as they should, allowing facilities to schedule predictive maintenance to replace parts before the system fails.
Handheld thermal imaging cameras rely on engineers having factory access, which has been difficult during the pandemic. Even in normal times it’s not always ideal to have additional personnel on-site, in peak periods such as Black Friday and Christmas.
The idea of manually scanning for maintenance guidance means that if the engineer misses a scan or fails to pick up an issue in time, your critical assets could fail.
Industry 4.0 – A novel solution for the automated prediction of maintenance
Our team has developed a fixed thermal imaging solution to continuously monitor the temperature of conveyor belts and drives. By mounting Teledyne FLIR cameras over conveyor belts in fixed locations, sites can continuously monitor part temperatures for signs of overheating.
The camera system automatically feeds live temperature data to our deep learning cloud server for instant analysis. The software stores, monitors and analyses machine-generated data in real-time, looking for patterns and changes over time. If the system finds that the drives or conveyor belt itself is heating up to exceed the set temperature threshold, the system will alert operators via email.
The data is also analysed to set predictive dates for maintenance. Pre-setting dates means essential maintenance and servicing is never missed.
Meeting e-commerce demands with deep learning
As e-commerce demands have increased, it’s more important than ever to keep on top of regularly scheduled maintenance to maintain and protect critical equipment.
This vision system can continuously take thermal images daily, instead of monthly, and analyse them for both wear and temperature changes against the set parameters.
By using deep learning software, it can begin to predict the future by learning patterns and time frames of wear. Instead of waiting to see wear after the event, you can now stop it before it happens.