Factories are filled with information, and not just data - valuable information that can help run the plant or business more effectively. However, the issue is one of transforming this information into something usable.
Machines create telemetry,
sensors measure temperatures and vibrations,
tracking systems report on asset location,
and production systems capture events during the manufacturing process.
The challenge is to get this information and transform it into something that can help drive decisions and actions in the factory, which is where
Artificial Intelligence (AI) + Internet of Things (IoT) comes in - or as it's sometimes called, AIoT.
From Sensors to Intelligence
A potential end-to-end for industrial data could involve something like:
Sensors -> Edge -> Data Platform -> AI/Analytics -> Operational Decisions
Where sensors and machines provide the information about
machine status,
temperature,
vibrations,
location,
production events,
equipment status,
etc. Edge computing can allow for some level of processing and reduce the amount of data flowing back to a data platform for further analysis. This can be combined with other data from manufacturing and enterprise systems.
Use Cases
Predictive maintenance is one area where AIoT can help. Instead of waiting for a failure to occur, manufacturers can evaluate sensor data to find patterns and detect anomalies that could indicate a potential issue. Other potential applications could include:
Production monitoring
Asset and tool tracking
Vehicle/VIN tracking
Intralogistics
Equipment monitoring
Manufacturing analytics
Process anomaly detection
The Challenge of Industrial AIoT
AIoT isn't simply about sensors or about an AI model - it's getting data from these sources and combining them with other information from the factory or enterprise, which can be a challenge given the variety of different legacy systems in many plants, such as various PLCs, SCADA systems, MES, ERP, databases, and more.
As such, building a reliable AIoT system involves looking at the entire data pipeline, from the machine itself and data acquisition, proper protocols, edge processing if needed, data processing and normalization, and how it'll be consumed securely within the wider system.
For additional information about AI, IoT, and smart industrial solutions, be sure to check out
OEMNex AI .
The interesting challenge of AIoT in manufacturing isn't simply "how do we get more data about the factory."
Rather, it's "how do we turn this data into something useful and actionable?"
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