Superior solutions for the IoT
SIMPLE, OPEN AND STANDARDISED CLOUD CONNECTIVITY
As information technology and automation technology continue to converge, cloud-based communication and data services are increasingly used in industrial automation projects. Beyond the scope of conventional control tasks, applications such as big data, data mining and condition or power monitoring enable the implementation of superior, forward-looking automation solutions. New hardware and software products from Beckhoff for Industry 4.0 and IoT ensure the simplest possible implementation of such advanced solutions.
Industry 4.0 and Internet of Things (IoT) strategies place strict requirements on the networking and communication capabilities of devices and services. In the traditional communication pyramid point of view, large quantities of data must be exchanged between field-level sensors and higher-level layers in these implementations. However, horizontal communication between PLC control systems also plays a critical role in modern production facilities. PC-based control technologies provide universal capabilities for horizontal communication and have become an essential part of present-day automation projects exactly for this reason. With the new TwinCAT IoT solution, the widely used TwinCAT 3 engineering and control software provides the ideal foundational technology for Industry 4.0 concepts and IoT communication. Moreover, new IoT-compatible I/O components from Beckhoff enable easy-to-configure and seamless integration into public and private cloud applications.
DEFINITION OF BUSINESS OBJECTIVES FOR INCREASING THE COMPETITIVE EDGE
Industry 4.0 and Internet of Things (IoT) applications do not start with just the underlying technology. In reality, the work begins much earlier than this. It is critically important when implementing IoT projects to first examine the corporate business objectives, establishing the benefits to be gained as a company from such projects. From an automation provider perspective, there are two distinct categories of customers that can be defined: machine manufacturers and their end customers – in other words, the end users of the automated machines.
In the manufacturing sector in particular, there is an obvious interest in reducing in-house production costs, both through efficient and reliable production control and also by reducing the number of rejects produced. The traditional machine manufacturer pursues very similar objectives, and above all is interested in reducing the cost of the machine while maintaining or even increasing production quality. Optimising the machine’s energy consumption and production cycles, as well as enabling predictive maintenance and fault diagnostics, can also be rewarding goals. The last two points in particular offer the machine manufacturer a solid basis to establish services that can be offered to end customers as an additional revenue stream. Of course, what both customer categories ultimately want is for the machine or product to be designed more attractively and to increase competitiveness in the marketplace.
COLLECTING, AGGREGATING AND ANALYSING PROCESS DATA
The process data used during production provides a foundation for creating added value and for achieving above-mentioned business objectives. This includes the machine values that are recorded by a sensor and transmitted via a fieldbus to the PLC. This data can be analysed directly on the controller for monitoring the status of a system using the TwinCAT condition monitoring libraries integrated in the TwinCAT 3 automation software, thereby reducing downtime and maintenance costs.
However, where there are several distributed controllers in production areas, it may not be sufficient to analyse data from a single controller. The aggregated data from multiple or even all controllers in a production system or a specific machine type is often needed to perform sufficient data analysis and make an accurate analytical statement about the overall system. However, the corresponding IT infrastructure is required for this purpose. Previous implementations focussed on the use of a central server system within the machine or corporate network that was equipped with data memory, often in the form of a database system. This allowed analysis software to access the aggregated data directly in the database in order to perform corresponding evaluations (Figure 1).
Although such an approach to realise data aggregation and analysis in production facilities certainly worked well, it presented a number of problems at the same time, since the required IT infrastructure had to be made available first. The fact that this gives rise to high hardware and software costs for the corresponding server system can be seen right away. However, the costs with respect to personnel should also not be overlooked: Because of the increasing complexity involved in networking production systems, especially with large numbers of distributed production locations, skilled personnel are necessary to successfully perform the implementation in the first place. To complicate matters, the scalability of such a solution is very low. Ultimately
the physical limits of the server system are reached at some point, be it the amount of memory available or the CPU power, or the performance and memory size required for analyses. This often resulted in more extensive, manual conversion work if systems had to be supplemented by new machines or controllers. At the end of the day, the central server system had to grow alongside in order to capably handle and process the additional data volume.
THE PATH TO THE PUBLIC CLOUD
Cloud-based communication and data services now avoid the aforementioned disadvantages by providing the user with an abstract view of the underlying hardware and software systems. “Abstract” in this context means that a user does not have to give any thought to the respective server system when using a service. Rather, only the use of the respective services has to be considered. All maintenance and update work on the IT infrastructure is performed on the part of the provider of a cloud system. Such cloud systems can be divided into public and private clouds.
The so-called public cloud service providers, such as Microsoft Azure or Amazon Web Services (AWS), for example, provide users with a range of services from their own data centres. This starts with virtual machines, where the actual user has control of the operating system and the applications installed on it, and stretches to abstracted communication and data services, which can be integrated by the user in an application. The latter, for example, also includes access to machine learning algorithms, which can make predictions and perform classifications regarding specific data states on the basis of certain machine and production information. The algorithms obtain the necessary contents with the aid of the communication services.
Such communication services are usually based on communication protocols, which in turn are based on the publish/ subscribe principle. This offers definite advantages from the resulting decoupling of all applications that communicate
with one another. On one