Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform
This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problems
Wu, G., Talwar, S., Johnsson, K., Himayat, N., Johnson, K. D. (2011). M2M: From mobile to embedded internet. IEEE Communications Magazine, 49 (4), 36–43. doi: https://doi.org/10.1109/mcom.2011.5741144
John, A., Igimoh, J. (2017). The Design of Wireless Sensor Network for Real Time Remote Monitoring of Oil & Gas Flow Rate Metering Infrastructure. International Journal of Science and Research, 6 (2), 425–429. Available at: https://www.ijsr.net/archive/v6i2/ART2017709.pdf
Khan, W. Z., Aalsalem, M. Y., Khan, M. K., Hossain, M. S., Atiquzzaman, M. (2017). A reliable Internet of Things based architecture for oil and gas industry. 2017 19th International Conference on Advanced Communication Technology (ICACT). doi: https://doi.org/10.23919/icact.2017.7890184
Wanasinghe, T. R., Gosine, R. G., James, L. A., Mann, G. K. I., Silva, O., Warrian, P. J. (2020). The Internet of Things in the Oil and Gas Industry: A Systematic Review. IEEE Internet of Things Journal, 7 (9), 8654–8673. doi: https://doi.org/10.1109/jiot.2020.2995617
Lalitha, K., Balakumar, V., Yogesh, S., Sriram, K. M., Mithilesh, V. (2020). IoT Enabled Pipeline Leakage Detection and Real Time Alert System in Oil and Gas Industry. International Journal of Recent Technology and Engineering, 8 (5), 2582–2586. doi: https://doi.org/10.35940/ijrte.e6380.018520
Oil & gas corporates are investing and partnering with IoT technology companies to develop analytics platforms, improve energy efficiency, and identify major infrastructure risks (2017). Available at: https://www.cbinsights.com/research/oil-gas-corporates-iot-activity-expert-intelligence/
SOCAR to submit new development strategy to government (2020). Available at: https://report.az/en/energy/socar-to-submit-new-development-strategy-to-government/
Grange, E.L. (2018). A Roadmap for Adopting a Digital Lifecycle Approach to Offshore Oil and Gas Production. Offshore Technology Conference. Houston. doi: https://doi.org/10.4043/28669-ms
Chauhan, N. (2018). Safety and health management system in Oil and Gas industry. Available at: http://www.hpaf.co.uk/wp-content/uploads/2018/01/Safety-and-Health-Management-System-in-Oil-and-Gas-Industry.pdf
Safety of Offshore Oil and Gas Operations Directive. Available at: https://energy.ec.europa.eu/topics/energy-security/offshore-oil-and-gas-safety/safety-offshore-oil-and-gas-operations-directive_en
Yasseri, S., Bahai, H. (2018). Safety in Marine Operations. International Journal of Coastal and Offshore Engineering, 2 (3), 29–40. doi: https://doi.org/10.29252/ijcoe.2.3.29
Sector profiles Oil and gas. Available at: https://toolkit.bii.co.uk/sector-profiles/oil-and-gas
SOCAR reports 2011–2018. Available at: https://www.socar.az/socar/en/economics-and-statistics/economics-and-statistics/socar-reports
Cumberland, S. (2019). The human factor of IoT in safety. Available at: https://www.plantengineering.com/articles/the-human-factor-of-iot-in-safety/
Silva, V. L., Kovaleski, J. L., Pagani, R. N., Corsi, A., Gomes, M. A. S. (2020). Human factor in smart industry: a literature review. Future Studies Research Journal: Trends and Strategies, 12 (1), 87–111. doi: https://doi.org/10.24023/futurejournal/2175-5825/2020.v12i1.473
Antonovsky, A., Pollock, C., Straker, L. (2013). Identification of the Human Factors Contributing to Maintenance Failures in a Petroleum Operation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 56 (2), 306–321. doi: https://doi.org/10.1177/0018720813491424
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., Kwak, K.-S. (2015). The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access, 3, 678–708. doi: https://doi.org/10.1109/access.2015.2437951
Majumder, S., Mondal, T., Deen, M. (2017). Wearable Sensors for Remote Health Monitoring. Sensors, 17 (12), 130. doi: https://doi.org/10.3390/s17010130
Castillejo, P., Martinez, J.-F., Rodriguez-Molina, J., Cuerva, A. (2013). Integration of wearable devices in a wireless sensor network for an e-health application. IEEE Wireless Communications, 20 (4), 38–49. doi: https://doi.org/10.1109/mwc.2013.6590049
Thibaud, M., Chi, H., Zhou, W., Piramuthu, S. (2018). Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: A comprehensive review. Decision Support Systems, 108, 79–95. doi: https://doi.org/10.1016/j.dss.2018.02.005
Mammadova, M. H., Jabrayilova, G. Z. (2020). Human factor of an health management system for shift workers in offshore oil and gas industry. Problems of information technology, 2, 13–31. doi: https://doi.org/10.25045/jpit.v11.i2.02
Mammadova, M. H., Jabrayilova, Z. G. (2021). Conceptual approaches to intelligent human factor management on offshore oil and gas platforms. ARCTIC Journal 74 (2), 19–40.
Mammadova, M. H., Jabrayilova, Z. G. (2021). Conceptual approaches to IoT-based personnel health management in offshore oil and gas industry. Proceedings of the7th International Conference on Control and Optimization with Industrial Applications (COIA 2020), Baku, Azerbaijan. Vol. 1. Baku, 257–259. Available at: http://coia-conf.org/upload/editor/files/COIA2020_V1.pdf
Ljoså, C. H., Tyssen, R., Lau, B. (2011). Mental distress among shift workers in Norwegian offshore petroleum industry – relative influence of individual and psychosocial work factors. Scandinavian Journal of Work, Environment & Health, 37 (6), 551–555. doi: https://doi.org/10.5271/sjweh.3191
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8 (3), 199–249. doi: https://doi.org/10.1016/0020-0255(75)90036-5
Melikhov, A. N., Bernshtein L. S., Korovin, S. Ya. (1990). Situational advising systems with fuzzy logic. Location: Moscow: Nauka, 272.
Bellman, R. E., Zadeh, L. A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17 (4), B–141–B–164. doi: https://doi.org/10.1016/j.procs.2013.05.063
Levin, V. I. (2001). A new generalization of operations on fuzzy sets. Journal of Computer and Systems Sciences International, 40 (1), 138–141. Available at: https://elibrary.ru/item.asp?id=14956908
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