The technological data collected by WebScada systems, which have existed and been in operation for decades, offer a wealth of opportunities that can be exploited by machine learning algorithms based on artificial intelligence, which have already been proven useful in the financial and telecommunications sectors.
The Rabbit Miner and Skyline apply a new data mining based research and development project to the Hungarian funding.
The aim of the Artificial Intelligence (MI Coalition) is to put Hungary at the forefront of artificial intelligence developments and applications in Europe and to become an important member of the international MI community.
Today, industrial plants and production sites record more and more data of the production due to the increasing sensor technology. However, only a minority of this data is used to increase production efficiency and excellence.
Industry 4.0 and the digital manufacturing revolution are all about collecting—and, more importantly, acting on—data gathered from the assembly process in real time.
Tamás Ruppert was attending the 7th International Conference on Industrial Engineering and Applications (ICIEA) in Paris from 15 to 17 January 2020. Tamás’s presentation was rated by the judges as the best presentation in its section.
We are in the top 50 small company at the startup competition in the USA. Unfortunately, thanks to the COVID-19, Gyula needs to move home, so we need to cancel our registration.
Human resources are still utilized in many manufacturing systems, so the development of these processes should also focus on the performance of the operators.
Dynamic cycle time setting and line balancing are the most significant problems in modular manufacturing.
A multilayer network model for the exploratory analysis of production technologies is proposed.
Industry 4.0-based human-in-the-loop cyber-physical production systems are transforming the industrial workforce to accommodate the ever-increasing variability of production.
P-graph-based multi-objective risk analysis and redundancy allocation in safety-critical energy systems
As most of the energy production and transformation processes are safety-critical, it is vital to develop tools that support the analysis and minimisation of their reliability-related risks.
The sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities.
Assembly line balancing improves the efficiency of production systems by the optimal assignment of tasks to operators.
Despite the high-pace improvement of industrial process automation, the management of abnormal events still requires human actions.
Hierarchical frequent sequence mining algorithm for the analysis of alarm cascades in chemical processes
Faults and malfunctions on complex chemical production systems generate alarm cascades that hinder the work of the operators and make fault diagnosis a complex and challenging task.
Understanding the importance of process alarms based on the analysis of deep recurrent neural networks trained for fault isolation
The identification of process faults is a complex and challenging task due to the high amount of alarms and warnings of control systems.
Due to the increasing automation and integrity of today’s productions systems, thousands of alarms are generated every day in the more and more complex process control units.
Towards operator 4.0, increasing production efficiency and reducing operator workload by process mining of alarm data
A methodology to extract temporal patterns of alarm sequences and operator actions from the log files of alarm management systems is proposed.
The operators of chemical technologies are frequently faced with the problem of determining optimal interventions.