CONDITION MONITORING / DIGITAL TWIN

Systems that you have already delivered to customers provide you with relevant operating and condition information via condition monitoring: on the basis of transmitted machine and sensor data, you gain real-time insights into the actual use of your machine(s). Aggregated observations in dashboards and data evaluations enable you to better understand correlations - for example between certain parameter characteristics and subsequent failures - and to improve your product accordingly. For example, you can analyse how certain environmental conditions such as temperature or humidity influence the performance.

You can make the data of individual machines available to your customers in the form of a digital twin. The customer thus receives a digital image of his machine that reflects the real actual state at all times. In the event of malfunctions, the customer can react more quickly and in a more targeted manner, as he can immediately see in the digital twin where the problem is located. In addition, you can use videos or instructions to help customers carry out minor repairs or install spare parts themselves and provide them with the collected data for their own use.

The added value for you as manufacturer: You gain initial experience with real-time data and get data-based insights into the performance of your products. You create the technical data setup for further use cases such as extended service, pay-per-use billing or predictive maintenance. And last but not least, you increase customer satisfaction.

The advantages for your customers: Customers receive a better product and benefit from higher availability (faster, targeted reactions in the event of malfunctions, help for self-help with repairs or replacement parts). The digital twin gives your customers the opportunity to better understand your machine, both in its entirety and in detail. This in turn improves your value creation process.

What data can be used? Basically, all possible sensor, machine and process data can be used for a condition monitoring scenario - e.g. wear of common consumable parts, temperatures, speeds, service life, failures, malfunctions (poor quality).

Which protocols are used? We use common industry standards such as MQTT or OPC UA.

How can the scenario be set up? The machine-side provision of data, e.g. via OPC UA, is the basis for the condition monitoring scenario. This data can then be loaded via the All for One IIoT Connector into a data lake based on the Azure Time Series. There, they can be analysed and/or aggregated and then uploaded to the SAP Commerce Cloud to display a digital twin.

Condition monitoring is the basis for many IoT scenarios: On the one hand, because the provision of data entails dealing with central technological topics such as connectivity, Big Data, etc.. On the other hand, real-time condition monitoring for many companies represents the first step towards data-driven optimisation - a step that is usually accompanied by a large learning curve. The All for One IIoT Connector can be a central part of this setup, as it enables you to map many more use cases.