The Industrial Internet of Things
With Retrofitting to IIoT
Very few companies are able to build a perfect production hall on the greenfield and follow the rules of IIoT to the letter. Automation and software companies have also become aware of this issue and respond to it by offering products for upgrading, retrofitting and retooling.
It is rarely the case that a factory is completely redesigned, built and equipped just to implement digital workflows and to set up a network of machines. The typical German medium-sized businesses in Germany produce in old locations and use a very heterogeneous machinery — both in terms of age and machine manufacturers. This presents companies and IIoT service providers with major challenges, since the aim is to successively network all machines and digitize processes.
Often, there is a general assumption that SMEs would oversleep the digital transformation or find it difficult to implement it. Looking at Deloitte's study "Industry 4.0 in small and medium-sized enterprises" from 2016, a somewhat more differentiated picture emerges: 28 % of companies are already completely networked, but with 29 %, almost as many companies have no networked infrastructure. Maybe it depends on the industry or product –however, this question was not investigated in the study. In the end, however, there remain two big questions to be answered:
- How can the existing machinery be upgraded or converted in such a way that the individual machines can be networked with each other?
- How do you implement the task of "installing" digital processes in a time- and cost-efficient manner with the existing employees?
A major challenge — now and in the future — lies in the different "lifetimes" of mechanics and software: The mechanics in machines are designed to last many years and decades, while software and IT systems quickly become obsolete. In order to network an outdated factory or production line and digitize the processes, the machines must become intelligent and their information and data must flow into intelligent software that creates added value. Existing control systems must also be retrofitted or upgraded. It is therefore not surprising that a large market for retrofitting solutions is emerging.
Automation companies in particular have already discovered this gap and are now offering products that are supposed to enable "Industry 4.0 for retrofitting". One of the first ways to connect a machine to digital capabilities was probably the Pepperl+Fuchs Smart Bridge. With this adapter, older sensors can not only send process data to the machine control system, but also communicate via Bluetooth with the higher-level systems which then evaluate all relevant data. For this purpose, the adapter is looped into the wiring between the sensor and the machine control, from where it picks up the sensor data without any interaction and establishes a wireless connection to a mobile device. The adapter does not require its own power connection, it uses the power from the sensor line. The readout sensor data is displayed on a tablet or smartphone via the smart bridge app. Using the app, users can also change the operating parameters of the sensors connected via smart bridge. Since data exchange takes place via a Bluetooth connection, there is no need to install additional cabling or interfere with the controller.
SKF's Enlight Quick Collect works completely without installation, but it should also make the work of maintenance engineers easier. The product consists of three components: a portable sensor, a mobile device and the Quick Collect app. The sensor collects machine data such as vibration and temperature values and transmits them to a smartphone or tablet using conventional mobile technology. The Quick Collect app installed there performs a basic analysis of the measurements in real time and informs the user immediately about possible problems. In addition, the app saves the findings for in-depth analysis and enables the forwarding of relevant information.
Making Machines Talk
The Bosch Rexroth IoT gateway also works without any intervention in the control system. Nevertheless, it should also be possible to network decades old existing machines with little effort. The inconspicuous box consists of the embedded control Indracontrol XM, which executes the software of the gateway. The architecture is based on open software with Linux as the operating system and a Java virtual machine that enables the deployment of Java applications. Process data such as temperature, pressure, vibration or power consumption can be recorded in real time via a sensor package from Bosch. Thanks to integrated software apps such as the device and processing app, users can easily customize the IoT gateway via a web-based platform.
What does the IoT Gateway do? It collects various sensor and control data and forwards them to a central database for analysis. This also works with older controllers without having to re-program them, as well as with third-party controllers from Beckhoff, Siemens and Rockwell. In order to obtain information or recommendations for action from the data, they are now connected to existing IT systems such as MES or special evaluation software. Thanks to its high connectivity, the IoT gateway can communicate with many higher-level IT systems just as easily as with cloud platforms.
Bosch Rexroth demonstrated the implementation of such a digitization application at Bosch Rexroth's Lohr plant using its own engine production facility and divided it into five exemplary steps:
1. Defining influencing factors: In order to optimize a machine, for example, certain parameters are important which influence the product quality.
2. Selection and retrofitting of the sensors: The information required for monitoring the machine must be collected by sensors in order to avoid machine failures.
3. Connecting the sensors to the IoT gateway: The collected sensor data is transferred to the higher-level IT systems via the gateway.
4. Connection of the higher-level IT systems: The processing app is used to determine the data transfer.
5. Exploit potential for improvement: New measures can be derived from the data to bring about improvements in production.
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