A new article at IoT World Today asks Is My Smart Factory Smarter Than Yours? It’s Hard to Say.
The latter part of the article explains how, when there’s a problem in a smart factory, it can have large affects. The onus is on technology that can predict problems before they cause downtime. This leads to questions where the data processing should be the observations that:
“In the long-run, pushing everything to the cloud doesn’t work from a cost point of view.”
“Once you aggregate and compress the data, for example, to ‘max,’ ‘min,’ ‘outliers,’ ‘average’ and stuff like that, you lose the ability to run data science”
Such situations are the focus of our new Sensor Cognition™ technology that can provide machine intelligence at the edge.
The Nordic blog has an informative post on How IoT-Based Predictive Maintenance Can Reduce Costs. It explains how connected sensors can save maintenance costs through reduced downtime. The post provides some examples from the power industry and explains how the same techniques can be used in the tools, retail, distribution and physical infrastructure industries.
As the post mentions, the challenge is how to scale this up. We are told IoT is the solution. Here at BeaconZone, we don’t believe IoT is always the solution, especially where there’s a requirement for higher sensor sampling frequencies. There’s too much data, too much data transfer and too much server processing. It really doesn’t scale. Apart from the waste and cost of these resources, the latency of triggering events based on the data is too high. Instead, look to so called ‘edge’ or ‘fog’ computing where more processing is done nearer the sensors and only pertinent data is sent to other systems.
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