Medium-sized machine and plant manufacturers in particular often do not have the opportunity to invest in secure cloud technology. T-Systems and Eaton support them in getting started. © Eaton

Medium-sized machine and plant manufacturers in particular often do not have the opportunity to invest in secure cloud technology. To enable them to position themselves successfully for the future, T-Systems and Eaton are developing simple, holistic and secure ways to integrate machines into IoT environments and exploit the potential of cloud services economically. On the one hand, the cooperation partners provide the technological building blocks for networking the systems via the cloud, and on the other, they develop digital solutions that can be tailored precisely to the customer's requirements.

The value of recorded machine data depends on how it is prepared and presented to the user. Users can only work with the data if they understand its significance. To achieve this, T-Systems and Eaton are developing methods and services to analyze and utilize this data. This includes, for example, visualizations, alarms depending on machine-specific threshold values or analyses to predetermine optimized maintenance cycles.

This data includes various process and status data: currents, switching cycles, energy consumption, temperatures and pressures as well as error messages from devices. The data collected from the machines and plant components in production is transferred to T-Systems via Eaton's controllers and visualization devices and transported to the cloud. Integrated encryption mechanisms ensure secure communication via the Internet.

The visualization and location-independent provision of data allows valuable insights to be gained, for example into the efficiency of production. Data can also be processed on a role-based basis so that, for example, a service technician is provided with different information to the production manager. The visualization module is developed in close coordination between the partners. This means that the display of the system in the cloud can be designed very similarly to the user interface of the machine and system manufacturer.

Targeted data analysis can also be used to determine failure probabilities over longer periods of time. This information can then be used to calculate and optimize maintenance cycles. The cost of maintenance work can be significantly reduced if unplanned visits by technicians are eliminated or local personnel take over planned work. Long-term data analysis can also be used to identify weak points in the design of the machine and to rectify them in a targeted manner based on the knowledge gained. In many cases, remote maintenance and remote control also replace the time-consuming and cost-intensive on-site deployment of service technicians when safe machine conditions prevail.

Comprehensive recording and analysis of machine and consumption data is the only way to check energy requirements, identify optimization opportunities and monitor the impact of implemented measures. Using the IoT solution, the machine manufacturer can obtain an up-to-date and detailed overview of all machine and system data, regardless of location. The most important service that a machine manufacturer must provide to its end customer is immediate assistance in the event of machine downtime and the rapid rectification of faults. However, previous online diagnostics often only provide the status of the system and, if necessary, the reason for the fault. Thanks to the IoT solution, historical data can now also be included.

Retrieve parameters from the cloud

If a new automation device is installed, the device's parameters can be uploaded to the cloud and saved via the IoT solution. If an identical system - anywhere in the world - is set up by the customer, they can retrieve the required parameters directly from the cloud instead of having to enter them into the system again. In order to detect anomalies in the operation of a machine or device, as much existing data as possible should be uploaded to the cloud solution. This includes data on the status of the machine or system, historical data and machine information from the manufacturer, maintenance data from the customer or data already stored in the cloud on the operation of the device. With the help of big data analyses and machine learning, a model can be created from this data that enables an in-depth understanding of the operating processes.

Dr. Stefan Braun, Head of Product Lifecycle & Digital Transformation Solution Consulting at T Systems, and Karl-Heinz Arndt, Manager Product Strategy at Eaton / am