The customer is a multinational group in the mechanical engineering sector. In the face of global competition, the customer's goal is to expand its products with applications based on machine data by implementing a digital strategy. The classic spectrum of sales drivers is to be expanded in this way.
The machines can provide data via their respective PLC (programmable logic controller). However, only a pure (permanent) read access is allowed, so that no manipulation or misconfiguration in the control and thus in the production process can occur. In addition, it must be possible to connect the machines during operation. In addition, it is not always clear from the outset what data is required and in what resolution for the respective application. Therefore, all data must first be collected and made available. In some cases, data must be collected and provided externally by means of sensors (retrofitted). All data from the PLC and from external sensors must be collected and pre-filtered locally. Due to very high sampling rates (up to 20ms) and thousands of variables per PLC that are measured, very large amounts of data per machine are generated (e.g. more than 1GB of data per day).
Based on azeti's fullstack AEP, an OEM solution has been developed for the customer specifically for applications in the field of production machines.
To create an interface for the extraction and pre-processing of data for Industry 4.0 applications, azeti's Edge Software was implemented as a standard application on an IoT Gateway. Once the gateway was connected to the machine controller, data from the PLC could be collected and processed directly. The standardization of the Edge made it possible to quickly connect a large number of machines. Both machines already installed in the field and newly built machines could be equipped with the same low effort.
After the machine data could be read out directly from the PLC, they were aggregated and analyzed locally. Critical problems or inefficient process sequences could thus be promptly detected and eliminated.
The data from all connected machines was sent via a secure connection to a central data hub based on azeti's Enterprise Cloud Stack. Here, both live data and historical data from various machines from all production sites worldwide can be merged and visualized. Special analysis tools allow, for example, evaluations with regard to machine performance and utilization or maintenance requirements. If a machine threatens to fail, the responsible employee can be alerted via the notification channels (email, SMS, Slack, etc.) or via a dashboard (available for desktop and mobile devices). By collecting a variety of machine data and creating a digital image of the machine, future events such as machine failures can be estimated with machine learning models.
A special feature is that the OEM solution for the machine manufacturer serves as the basis of his digital business model. By connecting machines, collecting and evaluating data and providing additional services such as visualization, alarm management and notifications, the customer was able to develop very application-specific applications and offer them to his customers in the production area.
A manufacturer of melting furnaces has used the azeti AEP to determine applications for metal level determination. The machine builder's customers were never able to determine exactly the optimum utilisation (filling quantity) of metal in the furnace. Based on the data collected by the melting furnace manufacturers and in conjunction with azeti's AEP, an app was developed specifically for filling the melting furnaces. This app was then made available to the melting furnace operators. This not only allowed the furnace to be filled with the optimum amount of metal, but also to optimise the utilisation and duration of the melting process and the use of operating materials (inert gases for firing).
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