Implementing cloud connectivity for IoT and Industrie 4.0
As information technology and automation technology continue to converge, cloud-based communication and data services are increasingly used in industrial automation projects. Compatible I/O components compatible with Industrial Internet of Things (IIoT) enable easy-to-configure and seamless integration into public and private cloud applications.
Beyond the scope of conventional control tasks, applications such as Big Data, data mining, and condition or power monitoring enable the implementation of advanced automation solutions. New hardware and software products for Industrie 4.0 and Industrial Internet of Things (IIoT) provide simple implementation for advanced solutions.
Industrie 4.0 and IIoT strategies place strict requirements on the networking and communication capabilities of devices and services. In the traditional communication pyramid in Figure 1, large quantities of data must be exchanged between field-level sensors and higher level layers in these implementations. However, horizontal communication between programmable logic controller (PLC) 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. Engineering and control software in PC-based control architectures provides the ideal foundational technology for Industrie 4.0 concepts and IoT communication. Moreover, new IIoT-compatible I/O components enable easy-to-configure and seamless integration into public and private cloud applications.
Creating a competitive edge with Industrie 4.0 and IoT
Industrie 4.0 and IoT applications do not start with just the underlying technology. In reality, the work begins much earlier than this. When implementing IoT projects, it is critical to examine the corporate business objectives first and establish the benefits that will be gained from these projects. From an automation provider's perspective, there are two distinct categories of customers that can be defined: machine manufacturers and the end customers of the automated machines.
In the manufacturing sector in particular, there is an interest in reducing in-house production costs, through efficient and reliable production control and also by reducing the number of rejects that are produced. The traditional machine manufacturer pursues very similar objectives and is interested in reducing the cost of the machine while maintaining or increasing production quality. Optimizing the machine's energy consumption and production cycles, as well as enabling predictive maintenance and fault diagnostics, are rewarding goals.
Specifically, predictive maintenance and fault diagnostics offer the machine manufacturer a solid basis to establish services that can be offered to end customers as an additional revenue stream. What both customer categories ultimately want is for the machine or product to be designed more efficiently and to increase competitiveness in the marketplace.
Collecting and analyzing process data
The process data that is used during production provides a foundation for creating added value and for achieving 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 analyzed directly on the controller for monitoring the status of a system using condition monitoring libraries integrated into leading automation software platforms, thereby reducing downtime and maintenance costs.
When several distributed controllers serve production areas, they may be insufficient to analyze data from a single controller. The collected data from multiple or even all controllers in a production system or a specific machine type is often needed to perform sufficient data analysis to properly analyze the overall system. A corresponding information technology (IT) infrastructure is required for this purpose. Previous implementations focused 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 collected data directly in the database to perform corresponding evaluations.
Although this approach to collect and analyze data in production facilities certainly worked well, it also presented a number of problems, since the required IT infrastructure had to be made available first. High hardware and software costs for the corresponding server systems were an issue, in addition to the cost of acquiring skilled personnel due to the implementation of complex networking production systems. To complicate matters, the scalability is very low. Ultimately, the physical limits of the server system are reached (amount of memory or 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 to handle and process the additional data volume.
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