A Novel Prognostics and Health Management Framework to Extract System Health Requirments in the Oil and Gas Industry
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Published
Jun 26, 2024
Khalid Alfahdi
Hakan Gultekin
Emad Summad
Abstract
The paramountcy of Prognostics and Health Management (PHM) within the oil and gas sector is instrumental in ensuring safety, reliability, and economic efficiency by optimizing system availability. However, a prevalent industrial challenge is the lack of a comprehensive identification of health management requirements from actual operational situations. This study introduces an innovative Prognostics and Health Management Framework (PHMF), encompassing a methodical procedure to discern health management necessities systematically. The PHMF consolidates structured causal factors, foundational elements of functional failure, and the antecedents of unplanned downtime, which collectively inform the PHM strategy.
This framework offers an integrated view of multiple dimensions of system health, facilitating accurate portrayal and proactive monitoring. It particularly underscores a reverse engineering approach to scrutinize the root causes of system failures and unexpected operational halts. To validate the practicality and efficacy of the PHMF, it has been applied to a real-world scenario: a lubrication oil system within a gas turbine equipment, thereby elucidating the specific PHM strategy prerequisites.
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Keywords
PHM, Oil and Gas, Framework, strategy, Reliability, asset management, functional failure, Downtime
References
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Alhazmi, N. (2018). A theoretical framework for physical asset management practices. Facilities, 36(3–4), 135–150. https://doi.org/10.1108/F-02-2016-0025/FULL/XML
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Booyse, W., Wilke, D. N., & Heyns, S. (2020). Deep digital twins for detection, diagnostics, and prognostics. Mechanical Systems and Signal Processing, 140, 106612. https://doi.org/10.1016/J.YMSSP.2019.106612
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Khan, S., Farnsworth, M., McWilliam, R., & Erkoyuncu, J. (2020). On the requirements of digital twin-driven autonomous maintenance. Annual Reviews in Control, 50, 13–28. https://doi.org/10.1016/J.ARCONTROL.2020.08.003
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Lee, J., Cameron, I., & Hassall, M. (2019). Improving process safety: What roles for Digitalization and Industry 4.0? Process Safety and Environmental Protection, 132, 325–339. https://doi.org/10.1016/J.PSEP.2019.10.021
Levitin, G., Xing, L., & Dai, Y. (2021). Optimal operation and maintenance scheduling in m-out-of-n standby systems with reusable elements. Reliability Engineering and System Safety, 211. https://doi.org/10.1016/J.RESS.2021.107582
Li, Y., He, Y., Ai, J., Wang, C., Han, X., Liao, R., & Yang, X. (2022). Functional health prognosis approach of multistation manufacturing system considering coupling operational factors. Reliability Engineering & System Safety, 219, 108211. https://doi.org/10.1016/J.RESS.2021.108211
Liu, C., Sun, J., Liu, H., Lei, S., & Hu, X. (2020). Complex engineered system health indexes extraction using low frequency raw time-series data based on deep learning methods. Measurement, 161, 107890. https://doi.org/10.1016/J.MEASUREMENT.2020.107890
Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M. (2020). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118, 103277. https://doi.org/10.1016/J.AUTCON.2020.103277
Lyu, J., Ying, R., Lu, N., & Zhang, B. (2020). Remaining useful life estimation with multiple local similarities. Engineering Applications of Artificial Intelligence, 95, 103849. https://doi.org/10.1016/J.ENGAPPAI.2020.103849
Mancuso, A., Compare, M., Salo, A., & Zio, E. (2021). Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems. Reliability Engineering & System Safety, 210, 107536. https://doi.org/10.1016/J.RESS.2021.107536
Manjurul Islam, M. M., Prosvirin, A. E., & Kim, J. M. (2021). Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines. Mechanical Systems and Signal Processing, 160, 107853. https://doi.org/10.1016/J.YMSSP.2021.107853
Melesse, T. Y., di Pasquale, V., & Riemma, S. (2020). Digital Twin Models in Industrial Operations: A Systematic Literature Review. Procedia Manufacturing, 42, 267–272. https://doi.org/10.1016/J.PROMFG.2020.02.084
Najari, F., Sobhanallahi, M. A., & Mohammadi, M. (2018). Improving maintenance strategy by physical asset management considering the use of MFOP instead of MTBF in Petrochemical. International Journal of Supply and Operations Management, 5(4), 319–337. https://doi.org/10.22034/2018.4.3
Nithin, A. H., Sriramula, S., & Ebinum, T. (2021). Reliability-centered maintenance and cost optimization for offshore oil and gas components. Journal of Physics: Conference Series, 1730(1), 012007. https://doi.org/10.1088/1742-6596/1730/1/012007
Nor, A. K. M., Pedapati, S. R., & Muhammad, M. (2021). Reliability engineering applications in electronic, software, nuclear and aerospace industries: A 20 year review (2000–2020). Ain Shams Engineering Journal. https://doi.org/10.1016/J.ASEJ.2021.02.015
Para, J., del Ser, J., Nebro, A. J., Zurutuza, U., & Herrera, F. (2019). Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0. Engineering Applications of Artificial Intelligence, 82, 30–43. https://doi.org/10.1016/J.ENGAPPAI.2019.03.022
Pliego Marugán, A., Peco Chacón, A. M., & García Márquez, F. P. (2019). Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines. Reliability Engineering & System Safety, 191, 106574. https://doi.org/10.1016/J.RESS.2019.106574
Poddar, T. (2018). Digital twins bridge intelligence among man, machine, and environment. Offshore Technology Conference Asia 2018, OTCA 2018. https://doi.org/10.4043/28480-MS Process Safety | AIChE. (n.d.). Retrieved February 20, 2023, from https://www.aiche.org/ccps/resources/glossary/processsafety-glossary/process-safety
Shin, J. H., & Jun, H. B. (2015). On condition-based aintenance policy. Journal of Computational Design and Engineering, 2(2), 119–127. https://doi.org/10.1016/J.JCDE.2014.12.006
Soualhi, M., Nguyen, K. T. P., & Medjaher, K. (2020). Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mechanical Systems and Signal Processing, 142, 106680. https://doi.org/10.1016/J.YMSSP.2020.106680
Syed, Z., & Lawryshyn, Y. (2020). Multi-criteria decision-making considering risk and uncertainty in physical asset management. Journal of Loss Prevention in the Process Industries, 65, 104064. https://doi.org/10.1016/J.JLP.2020.104064
Vrignat, P., Kratz, F., & Avila, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, 218, 108140. https://doi.org/10.1016/J.RESS.2021.108140
Wanasinghe, T. R., Wroblewski, L., Petersen, B. K., Gosine, R. G., James, L. A., de Silva, O., Mann, G. K. I., & Warrian, P. J. (2020). Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges. IEEE Access, 8, 104175–104197. https://doi.org/10.1109/ACCESS.2020.2998723
Yang, F., Habibullah, M. S., & Shen, Y. (2021). Remaining useful life prediction of induction motors using nonlinear degradation of health index. Mechanical Systems and Signal Processing, 148, 107183. https://doi.org/10.1016/J.YMSSP.2020.107183
Yingchao, L., Hu, X., & Zhang, W. (2019). Remaining useful life prediction based on health index similarity. Reliability Engineering & System Safety, 185, 502–510. https://doi.org/10.1016/J.RESS.2019.02.002
Yucesan, Y. A., Dourado, A., & Viana, F. A. C. (2021). A survey of modeling for prognosis and health management of industrial equipment. Advanced Engineering Informatics, 50, 101404. https://doi.org/10.1016/J.AEI.2021.101404
Zio, E. (2016). Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability, 65(4), 1769–1782. https://doi.org/10.1109/TR.2016.2591504
Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. https://doi.org/10.1016/J.RESS.2021.108119
Al-Anzi, F. S., Lababidi, H. M. S., Al-Sharrah, G., Al-Radwan, S. A., & Seo, H. J. (2022). Plant health index as an anomaly detection tool for oil refinery processes. Scientific Reports 2022 12:1, 12(1), 1–18. https://doi.org/10.1038/s41598-022-18824-2
AlMarzooqi, F. A., Hussain, M., & Ahmad, S. Z. (2019). Performance of physical asset management using the analytic hierarchy process. Property Management, 37(3), 327–345. https://doi.org/10.1108/PM-07-2018-0039/FULL/XML
Alhazmi, N. (2018). A theoretical framework for physical asset management practices. Facilities, 36(3–4), 135–150. https://doi.org/10.1108/F-02-2016-0025/FULL/XML
Campbell, J. D., & Jardine, A. K. S. ,. (2010). Asset Management Excellence: Optimizing Equipment Lifecycle Decisions. In J. McGlynn (Ed.), Asset Management Excellence (Second Edition). CRC Press. https://doi.org/10.1201/B10361/ASSETMANAGEMENT-EXCELLENCE-JOHN-CAMPBELLANDREW-JARDINE-JOEL-MCGLYNN
Biggio, L., & Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/FRAI.2020.578613/FULL
Booyse, W., Wilke, D. N., & Heyns, S. (2020). Deep digital twins for detection, diagnostics, and prognostics. Mechanical Systems and Signal Processing, 140, 106612. https://doi.org/10.1016/J.YMSSP.2019.106612
Bougacha, O., Varnier, C., & Zerhouni, N. (2020). Enhancing Decisions in Prognostics and Health Management Framework. International Journal of Prognostics and Health Management, 11(1), 1–19. https://doi.org/10.36001/IJPHM.2020.V11I1.2607
Choo, B. Y., Adams, S. C., Weiss, B. A., Marvel, J. A., & Beling, P. A. (2016). Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems. International Journal of Prognostics and Health Management, 7(3). https://doi.org/10.36001/IJPHM.2016.V7I3.2412 Continuous Improvement Model - Continual Improvement Tools | ASQ. (n.d.). Retrieved January 22, 2023, from https://asq.org/quality-resources/continuousimprovement
GAO, G., ZHOU, D., TANG, H., & HU, X. (2021). An Intelligent Health diagnosis and Maintenance Decision-making approach in Smart Manufacturing. Reliability Engineering & System Safety, 216, 107965. https://doi.org/10.1016/J.RESS.2021.107965
Hanachi, H., Mechefske, C., Liu, J., Banerjee, A., & Chen, Y. (2018). Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey. IEEE Transactions on Reliability, 67(3), 1340–1363. https://doi.org/10.1109/TR.2018.2822702
Hoffmann Souza, M. L., da Costa, C. A., de Oliveira Ramos, G., & da Rosa Righi, R. (2020). A survey on decision-making based on system reliability in the context of Industry 4.0. Journal of Manufacturing Systems, 56, 133–156. https://doi.org/10.1016/J.JMSY.2020.05.016
Hu, Y., Miao, X., Si, Y., Pan, E., & Zio, E. (2022). Prognostics and health management: A review from the perspectives of design, development, and decision. Reliability Engineering & System Safety, 217, 108063. https://doi.org/10.1016/J.RESS.2021.108063
Hunszu Liu, Sheue-Ling Hwang, Thu-Hua Liu & Guang-Hann Chen (2004) IMPLEMENTATION OF HUMAN ERROR DIAGNOSIS (HED) SYSTEM, Journal of the Chinese Institute of Industrial Engineers, 21:1, 82-91, DOI: 10.1080/10170660409509390
ISO - ISO 14224:2006 - Petroleum, petrochemical and natural gas industries — Collection and exchange of reliability and maintenance data for equipment. (n.d.). Retrieved January 22, 2023, from https://www.iso.org/standard/36979.html
Khan, S., Farnsworth, M., McWilliam, R., & Erkoyuncu, J. (2020). On the requirements of digital twin-driven autonomous maintenance. Annual Reviews in Control, 50, 13–28. https://doi.org/10.1016/J.ARCONTROL.2020.08.003
Kirwan, B. (1994). A Guide to Practical Human Reliability Assessment. London: CRC Press, https://doi.org/10.1201/9781315136349
Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 100041. https://doi.org/10.1016/J.EGYAI.2020.100041
Lee, J., Cameron, I., & Hassall, M. (2019). Improving process safety: What roles for Digitalization and Industry 4.0? Process Safety and Environmental Protection, 132, 325–339. https://doi.org/10.1016/J.PSEP.2019.10.021
Levitin, G., Xing, L., & Dai, Y. (2021). Optimal operation and maintenance scheduling in m-out-of-n standby systems with reusable elements. Reliability Engineering and System Safety, 211. https://doi.org/10.1016/J.RESS.2021.107582
Li, Y., He, Y., Ai, J., Wang, C., Han, X., Liao, R., & Yang, X. (2022). Functional health prognosis approach of multistation manufacturing system considering coupling operational factors. Reliability Engineering & System Safety, 219, 108211. https://doi.org/10.1016/J.RESS.2021.108211
Liu, C., Sun, J., Liu, H., Lei, S., & Hu, X. (2020). Complex engineered system health indexes extraction using low frequency raw time-series data based on deep learning methods. Measurement, 161, 107890. https://doi.org/10.1016/J.MEASUREMENT.2020.107890
Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M. (2020). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118, 103277. https://doi.org/10.1016/J.AUTCON.2020.103277
Lyu, J., Ying, R., Lu, N., & Zhang, B. (2020). Remaining useful life estimation with multiple local similarities. Engineering Applications of Artificial Intelligence, 95, 103849. https://doi.org/10.1016/J.ENGAPPAI.2020.103849
Mancuso, A., Compare, M., Salo, A., & Zio, E. (2021). Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems. Reliability Engineering & System Safety, 210, 107536. https://doi.org/10.1016/J.RESS.2021.107536
Manjurul Islam, M. M., Prosvirin, A. E., & Kim, J. M. (2021). Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines. Mechanical Systems and Signal Processing, 160, 107853. https://doi.org/10.1016/J.YMSSP.2021.107853
Melesse, T. Y., di Pasquale, V., & Riemma, S. (2020). Digital Twin Models in Industrial Operations: A Systematic Literature Review. Procedia Manufacturing, 42, 267–272. https://doi.org/10.1016/J.PROMFG.2020.02.084
Najari, F., Sobhanallahi, M. A., & Mohammadi, M. (2018). Improving maintenance strategy by physical asset management considering the use of MFOP instead of MTBF in Petrochemical. International Journal of Supply and Operations Management, 5(4), 319–337. https://doi.org/10.22034/2018.4.3
Nithin, A. H., Sriramula, S., & Ebinum, T. (2021). Reliability-centered maintenance and cost optimization for offshore oil and gas components. Journal of Physics: Conference Series, 1730(1), 012007. https://doi.org/10.1088/1742-6596/1730/1/012007
Nor, A. K. M., Pedapati, S. R., & Muhammad, M. (2021). Reliability engineering applications in electronic, software, nuclear and aerospace industries: A 20 year review (2000–2020). Ain Shams Engineering Journal. https://doi.org/10.1016/J.ASEJ.2021.02.015
Para, J., del Ser, J., Nebro, A. J., Zurutuza, U., & Herrera, F. (2019). Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0. Engineering Applications of Artificial Intelligence, 82, 30–43. https://doi.org/10.1016/J.ENGAPPAI.2019.03.022
Pliego Marugán, A., Peco Chacón, A. M., & García Márquez, F. P. (2019). Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines. Reliability Engineering & System Safety, 191, 106574. https://doi.org/10.1016/J.RESS.2019.106574
Poddar, T. (2018). Digital twins bridge intelligence among man, machine, and environment. Offshore Technology Conference Asia 2018, OTCA 2018. https://doi.org/10.4043/28480-MS Process Safety | AIChE. (n.d.). Retrieved February 20, 2023, from https://www.aiche.org/ccps/resources/glossary/processsafety-glossary/process-safety
Shin, J. H., & Jun, H. B. (2015). On condition-based aintenance policy. Journal of Computational Design and Engineering, 2(2), 119–127. https://doi.org/10.1016/J.JCDE.2014.12.006
Soualhi, M., Nguyen, K. T. P., & Medjaher, K. (2020). Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mechanical Systems and Signal Processing, 142, 106680. https://doi.org/10.1016/J.YMSSP.2020.106680
Syed, Z., & Lawryshyn, Y. (2020). Multi-criteria decision-making considering risk and uncertainty in physical asset management. Journal of Loss Prevention in the Process Industries, 65, 104064. https://doi.org/10.1016/J.JLP.2020.104064
Vrignat, P., Kratz, F., & Avila, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, 218, 108140. https://doi.org/10.1016/J.RESS.2021.108140
Wanasinghe, T. R., Wroblewski, L., Petersen, B. K., Gosine, R. G., James, L. A., de Silva, O., Mann, G. K. I., & Warrian, P. J. (2020). Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges. IEEE Access, 8, 104175–104197. https://doi.org/10.1109/ACCESS.2020.2998723
Yang, F., Habibullah, M. S., & Shen, Y. (2021). Remaining useful life prediction of induction motors using nonlinear degradation of health index. Mechanical Systems and Signal Processing, 148, 107183. https://doi.org/10.1016/J.YMSSP.2020.107183
Yingchao, L., Hu, X., & Zhang, W. (2019). Remaining useful life prediction based on health index similarity. Reliability Engineering & System Safety, 185, 502–510. https://doi.org/10.1016/J.RESS.2019.02.002
Yucesan, Y. A., Dourado, A., & Viana, F. A. C. (2021). A survey of modeling for prognosis and health management of industrial equipment. Advanced Engineering Informatics, 50, 101404. https://doi.org/10.1016/J.AEI.2021.101404
Zio, E. (2016). Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability, 65(4), 1769–1782. https://doi.org/10.1109/TR.2016.2591504
Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. https://doi.org/10.1016/J.RESS.2021.108119
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