Anyone who visited one of the major trade fairs this year was almost overwhelmed by the megatrends that were in focus. Automation and networking, artificial intelligence (AI), big data and cloud-based solutions are just some of the topics that are currently occupying us. But renewable energies, hydrogen technologies and sustainability also play a major role. All of this depends on future-proof measurement technology, as the following article describes.
Linked to the drive for greater sustainability, the implementation of a CO2-neutral energy supply and the development of alternative drive technologies are high on the agenda. Entire industries are being revolutionized in these areas and new business fields are being opened up. A good example of this is the development of the entire hydrogen value chain, in which new technologies will be used, some of which are still in the development phase. There is still a lot to do here. The topic of sustainability is now firmly established, but the area of artificial intelligence (AI) is currently having a greater impact. Many companies are asking themselves how AI can be used to optimize business processes or create added value for customers. In this context, the service concept is also becoming increasingly important under the motto "Everything as a service". But how do these current trends affect measurement technology? It is astonishing how fundamental the reliable recording and analysis of measurement data actually is in these areas.
MEASUREMENT TECHNOLOGY AND SUSTAINABILITY - DO THEY GO TOGETHER?
Sustainability and climate neutrality are global megatrends that have an impact on almost all areas of technology. The conversion to CO2-neutral energy supply and emission-free drive technologies is leading to a need for development in many different industries. Both in R&D and in product testing, this results in a wide range of measurement tasks that need to be solved using state-of-the-art measurement technology. One example of this is the entire hydrogen technology value chain. Here, test benches for electrolysers, test systems for hydrogen pipes and tanks or measurement technology for testing fuel cells and lithium-ion batteries are required.
For the metrological equipment of such systems, Delphin Technology offers suitable complete solutions from a single source. These consist of intelligent measurement technology hardware for the precise recording, pre-processing and storage of measured values, central measurement data management software for the synchronization and joint processing of decentrally recorded data and platform-independent visualization and analysis software that displays measurement and process data in a variety of diagram types and individually configurable dashboards. In addition to the newly emerging measurement technology tasks, the company is also involved in many projects in the field of sustainable technologies. For example, the company's measurement technology solutions are used in the field of vibration monitoring in hydropower plants at many different locations around the world. Also in the field of Service life test and optimization, the company makes a major contribution to the development of sustainable and durable products. Efficiency optimization is another example where measurement technology is used to optimize systems and machines with the aim of reducing energy consumption.
SO WHAT'S NEW?
The development of AI technologies is progressing at a tremendous pace. Established forms of AI-based condition monitoring often use the principle of "supervised learning". This requires extensive training data sets, which are previously labeled as "good" or "bad" and then made available to the model. The model then compares the current status with these sample data sets and evaluates the current status of the machine or system. Some newer models, on the other hand, are capable of so-called "unsupervised learning". Here, training data sets are no longer required. Instead, the model is able to cluster unlabeled data records without human intervention and thus recognize hidden patterns itself. With these models, the learning phase can be significantly shortened. In addition, the sometimes time-consuming phase of generating "good" or "bad" data sets is no longer necessary.
Such modern AI algorithms can also be used to solve complex tasks in the areas of condition monitoring and predictive maintenance. Modern AI algorithms demonstrate their strengths particularly when there are a large number of influencing variables that can have an impact on the wear of a machine or the quality of a manufactured product.
ARTIFICIAL INTELLIGENCE AND CLIMATE NEUTRALITY HAVE IMPACT ON ALMOST ALL TECHNOLOGY AREAS
Image: ©Gorodenkoff/stock.adobe.com / Delphin Technology AG