Most scalable IIoT solutions are based on a platform on which data is collected. For this purpose, production level data - if necessary, with an intermediate step via edge devices - must be transferred to a cloud platform and collected centrally. To make them read- and understandable, the data is put into an integrated context with the help of reporting functions. This makes it possible to establish dependencies between the data, the various production areas, product quality and machine availability. In the next step, the application of analytical functions helps to gain insights, for example to achieve improvements in product quality.
IBM complements these solutions with two elements: From predictive maintenance you move to prescriptive maintenance, i.e. it is not only said what happens, but what is the best action. The second element is the analysis of unstructured data. Unstructured data can take the form of images, manuals, machine repair tickets, flyers, Post-its, etc. The analysis system is taught-in, i.e. initially filled with structured and unstructured data. Depending on the quality and quantity of the data, the initialization takes different lengths of time. The result is faster and more flexible quality assurance and production optimization.
In a nutshell you could consider the latter as a journey from data collection to big data, transfer to smart data, upon which new business models and new revenue streams could be established.
One example is Cognitive Visual Inspection. The basis is an algorithm for pattern recognition. It is taught what pictures of good and bad parts look like. Know-how from production, quality assurance, production and design data and external data such as the weather are taken into account. The parts in production are detected by a camera and analyzed for quality defects in (almost) real time. Unlike an automated classic visual inspection, Watson learns, improves with each day and does not need to be readjusted.
This lecture presents various application scenarios that are also suitable for small and medium-sized enterprises and different batch sizes and complexities. The range of digitization of industry (Industry 4.0) and current developments in the field of artificial intelligence is stretched.
Furthermore, a context is given from current to future capabilities in the area of hardware and software in IT, covering the background of new Hardware- and Software paradigms, like Quantum- and Neuromorphic computing, Blockchain and embedded Security.ÜCRETSİZ kaydolun