Mining the raw material of the future!
The compact IGW/941 edge gateway from SSV Software Systems GmbH brings machine learning to enclosures, helping users gather valuable data close to the source and use it to boost profits.20 Mar. 2019
As we approach the end of the second decade of this new millennium, there is one "raw material" that is proving more valuable and more profitable than anything Mother Earth has given us since time immemorial - data. Often simply generated as a constant by-product in every machine, system and process, this valuable treasure trove can be leveraged using suitable hardware and software and then converted into useable information through machine learning (ML). This in turn can be used to devise predictive maintenance concepts, predictive quality, predictive efficiency and ML-based anomaly detection processes. However, when it comes to the Industrial Internet of Things (IIoT), you first need special sensors to generate, connect together and prepare this "raw material". Only then can information be mined using corresponding ML algorithms. The resultant "refined raw material" can then be put to good use, both locally - for example, using OPC UA - and on a location-independent basis with the aid of a cloud.
At HANNOVER MESSE 2019, SSV Software Systems GmbH is showcasing its new IGW/941 - a compact edge gateway with pre-installed ML algorithms and a wide range of data science components for industrial applications. The IGW/941 allows users to create applications that, for instance, collect sensor data, convert it into information through classification or regression, and pass on the result via OPC UA or MQTT. The IGW/941 features preconfigured and ready-to-use development tools for the ML training phase and modeling. What's more, SSV offers all IGW/941 users a webinar with the following content: "Basic principles and terminology of machine learning", "A complete machine learning process, including sensor data capture, data preparation, modeling and model assessment", "Determining model accuracy and adapting hyperparameters" and "Combining the output of a machine learning algorithm with other systems".
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