Machine Learning-Supported Designing of Human–Machine Interfaces
Source abstract
Abstract
The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Flexible and Reconfigurable Manufacturing Systems · Business Process Modeling and Analysis · Digital Transformation in Industry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, H-8200 Veszprém, Hungary
Funding
Ministry of Culture and Innovation of Hungary
References (31)
Show source-provided references
1. Kamizi, S.A. (2021, January 28–30). UI/UX of Human-Machine Interface for Industrial Application: Review and Preleminary Design. Proceedings of the FCSIT UNIMAS FYP Symposium, Kota Samarahan, Malaysia.
2. Machine learning applications in production lines: A systematic literature review10.1016/j.cie.2020.106773 ↗
3. Kistan, T., Gardi, A., and Sabatini, R. (2018). Machine learning and cognitive ergonomics in air traffic management: Recent developments and considerations for certification. Aerospace, 5.10.3390/aerospace5040103 ↗
4. Pérez-Reynoso, F., Farrera-Vazquez, N., Capetillo, C., Méndez-Lozano, N., González-Gutiérrez, C., and López-Neri, E. (2022). Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot. Sensors, 22.10.3390/s22093424 ↗
5. A smart PLC-SCADA framework for monitoring petroleum products terminals in industry 4.0 via machine learning10.1177/00202940221103305 ↗
6. Industry 5.0 and its technologies: A systematic literature review upon the human place into IoT- and CPS-based industrial systems10.1016/j.cie.2023.109426 ↗
7. Mourtzis, D., Angelopoulos, J., and Panopoulos, N. (2023). The Future of the Human-Machine Interface (HMI) in Society 5.0. Future Internet, 15.10.3390/fi15050162 ↗
8. Hollifield, B. (2012, January 7–9). A High Performance HMI: Better Graphics for Operations Effectiveness. Proceedings of the 2012 Water/Wastewater and Automation Controls Symposium, Orlando, FL, USA.
9. Data-driven industrial human-machine interface temporal adaptation for process optimization
10. A Smart Factory Architecture Based on Industry 4.0 Technologies: Open-Source Software Implementation10.1109/ACCESS.2023.3316116 ↗
11. Sun, G., Xu, P., Guo, M., Sun, H., Du, Z., Li, Y., and Zhou, B. (2022). OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log. Mathematics, 10.10.3390/math10132292 ↗
12. Human-robot collaboration empowered by hidden semi-Markov model for operator behaviour prediction in a smart assembly system10.1016/j.jmsy.2021.12.001 ↗
13. Formal models for high performance HMI engineering10.3182/20120215-3-AT-3016.00151 ↗
14. A Methodology for the Systematic Selection of Human-Machine Interface Device Types in Production Machinery Development10.1016/j.procir.2023.02.173 ↗
15. Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions10.1109/ACCESS.2022.3182491 ↗
16. Data acquisition and monitoring system framed in Industrial Internet of Things for PEM hydrogen generators10.1016/j.iot.2023.100795 ↗
17. (2015). Human Machine Interfaces for Process Automation Systems (Standard No. ANSI-ISA-101.01-2015).
18. Frequent pattern mining-based log file partition for process mining10.1016/j.engappai.2023.106221 ↗
19. Beyerer, J., Maier, A., and Niggemann, O. (2020). Machine Learning for Cyber Physical Systems, Springer.10.1007/978-3-662-59084-3 ↗
20. Partovian, S., Bucaioni, A., Flammini, F., and Thornadtsson, J. (2023). Analysis of log files to enable smart-troubleshooting in Industry 4.0: A systematic mapping study. IEEE Access.10.1109/ACCESS.2023.3342365 ↗
21. van der Aalst, W. (2016). Process Mining: Data Science in Action, Springer.10.1007/978-3-662-49851-4 ↗
22. Sequence Compression and Alignment-Based Process Alarm Prediction10.1021/acs.iecr.3c00935 ↗
23. A general methodology for adapting industrial HMIs to human operators10.1109/TASE.2019.2941541 ↗
24. Network science
25. Shchur, O., and Günnemann, S. (2019). Overlapping community detection with graph neural networks. arXiv.
26. Overlapping Community Detection in Directed and Undirected Attributed Networks Using a Multiobjective Evolutionary Algorithm10.1109/TCYB.2019.2931983 ↗
27. Fournier-Viger, P., Lin, C., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., and Lam, H.T. (2016). Machine Learning and Knowledge Discovery in Databases, Proceedings of the 19th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2016), Riva del Garda, Italy, 19–23 September 2016, Springer.
28. Fournier-Viger, P., Gomariz, A., Campos, M., and Thomas, R. (2014). Advances in Knowledge Discovery and Data Mining, Proceedings of the 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, 13–16 May 2014, Springer.
29. Discovering social networks from event logs10.1007/s10606-005-9005-9 ↗
30. Nielsen, J. (1994, January 24–28). Usability inspection methods. Proceedings of the Conference Companion on Human Factors in Computing Systems, Boston, MA, USA.10.1145/259963.260531 ↗
31. Frequent pattern mining in multidimensional organizational networks10.1038/s41598-019-39705-1 ↗