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The Foundation Basic Data Connectivity
Its genesis was seeking a solution to one mission-critical problem—connection and protocol conversion. The ‘universal translator’ was the industrial gateway that spoke all of these languages. It consolidated raw data from various sources at the shop floor level and sent it to a central system, such as other SCADAs or cloud systems. This was a significant step in the direction of digitization because it rendered data visual and operational. The brains (the processing and analysis) were centralised, usually causing latency and bandwidth issues.
Embedding Edge Computing: A Jit and Go Moment
And in industrial applications, which require millisecond response times and more distributed control, the role of the gateway needed to expand. And the data passer-by became a smart edge computing device. Industrial gateways today have plenty of processing power, memory and storage. That allows them to do more than just collect and pass along data, but also to process intelligence and execute applications at the source on or near the edge of a network.
The processing of critical data is no more round trip to the cloud enabled with such powerful integrations of edge computing performance. The gateway can filter, cleans and aggregate raw data on-the-fly. By supporting AI to be done locally, a solution could run such lightweight algorithms that can detect and respond behaviours or anomalies in real-time (i.e., issuing sounds alerts, controlling machinery) without the DPC delay. This transparent processing offloads the network and central systems, allowing the upstream to only carry useful and condensed data for being studied locally or archived on a longer term basis.
Driving Intelligence with Integrated Analytics
Analytics being built into the gateway is the big thing here. But even more than just a simple pre-processing, it is worth noting that the gateways can now be the richest place in terms of analytic models. They can perform real-time OEE (Overall Equipment Effectiveness), energy use patterns or simply run machine learning models for predictive maintenance.
For instance, rather than sending a massive amount of vibration data from an engine to cloud, the smart gateway can process the high-frequency signal data locally. It can learn heuristics that flash warning signs for an impending shutdown, immediately alerting a repair crew but sending only a high-level notice to the central database. This capability converts raw data into actionable information at the edge, which in turn creates proactive decision making rather than relative response and avoidance of lost time.
Enabling Smarter Industrial Applications
This shift from a connectivity tool to a smart edge node is the basis for smarter industrial use cases.
Predictive Maintenance. Since the health of equipment is being always monitored on-line and in real time, maintenance action is taken only when required to prolong life span.
Process Optimization in Real-time. Gateways are configured to adjust the machine parameters on the fly based on instantaneous sensor response and this ensures better product quality and consistency.
Improved Production Efficiency. By having data analysis at the edge of an IoT system, real-time view into production bottlenecks or waste is gained, which allows faster decision action.
It’s Smart, it’s Distributed and it is the Future
The long and circuitous gestation period of the industrial gateway is reflective in part of the larger drift by the industry toward a more intelligent, distributed infrastructure. And now it's no longer just a data pipeline, it becomes a smart hub and the factory to be more reactive, more efficient and even smarter. At Shanghai Smawave Technology Co., ltd, we are at the forefront of this revolution with our IoT-ready gateway solutions which are enabling our partners to harness their industrial data.