ارائه یک الگوی توسعه یافته به منظور تخمین شاخص سلامت تجهیزات (مطالعه موردی: تجهیز تونل باد)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین

2 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین (ع)

3 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین ، تهران، ایران

چکیده

 شاخص سلامت، ابزاری به منظور ارزیابی وضعیت کارکردی یک تجهیز با هدف بهبود عملکرد عملیاتی آن است. در این تحقیق انواع مدل­های موجود در زمینه تخمین شاخص­های سلامت به همراه چالش­ها و محدودیت­های آن­ها، مورد بررسی قرار گرفت که براساس آن یک الگوی توسعه‌یافته با پنچ گام اصلی شامل مراحل انتخاب تجهیز، ارزیابی اثر عوامل بارگذاری و موقعیت مکانی، محاسبه نرخ پیری، محاسبه شاخص سلامت اولیه، و در نهایت محاسبه و ارزیابی اصلاح­گرهای شاخص سلامت و قابلیت اطمینان پیشنهاد شد. الگوی پیشنهادی در پنج نوع فن مختلف از یک تجهیز تونل باد، با توجه به هزینه­های قابل ­توجه نگهداشت در آن­ها پیاده­سازی شد. نتایج حاصل از شاخص سلامتِ فن­ها در قالب یک طرح گرافیکی نشان داد که امکان تعیین روند سرعت نرخ خرابی­ها و عمر کارکردی تجهیزات وجود دارد. همچنین نتایج حاصل از این تحقیق می­تواند مورد استفاده در انتخاب استراتژی­ مناسب نگهداشت با هدف بهبود عملکرد عملیاتی تجهیزات باشد.

کلیدواژه‌ها


عنوان مقاله [English]

A Developed Framework for Estimating the Equipment' Health Indicator: A Study for Wind Tunnel Equipment

نویسندگان [English]

  • Saeed Ramezani 1
  • Hamzeh Soltanali 2
  • Omid Bayat 3
1 Department of Industrial Engineering, Faculty of Engineering, Imam Hossein University (IHU), Tehran, Iran
2 Department of Industrial Engineering, Faculty of Engineering, Imam Hossein University (IHU), Tehran, Iran
3 Department of Industrial Engineering, Faculty of Engineering, Imam Hossein University (IHU), Tehran, Iran
چکیده [English]

Health index is a tool to evaluate the functional condition of an equipment with the aim of improving its operational performance. In this research, the types of models available in the field of asset health index estimation along with their challenges and limitations were examined, based on a developed model. The proposed model was implemented in five different types of wind tunnel equipment fans, due to their significant maintenance and repair costs. The model proposed in this research in order to estimate the health index includes steps such as: 1) selecting the equipment and defining its class, 2) evaluating the effect of loading factors and location, 3) calculating the aging rate, 4) achieving the initial health index at age t and 5) evaluation of load effect, modifiers of health index and reliability, and calculation of current health index. Examining the values obtained from the fan health index, in the form of a graphic design, showed that it is possible to determine the speed of failure rate and the functional life of the equipment. The results of this research can be used in choosing the appropriate strategy for maintenance and repairs with the aim of improving the operational performance of other equipment.

کلیدواژه‌ها [English]

  • Health index
  • Equipment
  • Developed model
  • Maintenance
  • Wind tunnel turbine
  • Ahmadi, S.H., & Grossi Mokhtarzade, N. (2012). Investigating and prioritizing the sensitivity of devices for preventive maintenance and repairs with the Martel and Zaras model. Journal of Industrial Management, (3) 5, 1-2. (in Persian).

 

  • Durán, O., Orellana, F., Perez, P., & Hidalgo, T. (2020). Incorporating an asset health index into a life cycle costing: A proposition and study case. Mathematics, 8(10), 1787.
  • Hastings, N. (2010). Physical Asset Management. Second Edition, Springer Pulisher
  • Zille, V., Berenguer. C., Grall, A., & Depujols, A. (2010). Simulation of maintained multicomponent systems for dependability analysis. In A. Faulin, S. Martorell, & J. Ramirez-Márquez (Eds.) Simulation methods for reliability and availability of complex systems. Berlin: Springer (Chapter 12). (pp. 253–272).
  • ISO 55002. International Organization for Standardization (2014). Asset Management – Management System – Guidelines for the application of ISO 55001.
  • Crespo Del Castillo, A., Sasidharan, M., Nentwich, C., Merino, J., & Parlikad, A. (2023). Data-driven Asset Health Index–an application to evaluate quay cranes in container ports.
  • Candón, E., Crespo Márquez, A., Guillén, A., & Leturiondo, U. (2022). Challenges on an Asset Health Index Calculation. In World Congress on Engineering Asset Management (pp. 205-216). Cham: Springer International Publishing.
  • de la Fuente, A., Crespo, A., Sola, A., Guillén, A., Gómez, J., & Amadi-Echendu, J. E. (2021). Planning major overhaul and equipment renovation based on asset criticality and health index. In 14th WCEAM Proceedings (pp. 83-90). Springer International Publishing.
  • Rediansyah, D., Prasojo, R. A., & Abu-Siada, A. (2021). Artificial intelligence-based power transformer health index for handling data uncertainty. IEEE Access, 9, 150637-150648.
  • Manninen, H., Kilter, J., & Landsberg, M. (2021). Health index prediction of overhead transmission lines: a machine learning approach. IEEE Transactions on Power Delivery, 37(1), 50-58.
  • Zeinoddini-Meymand, H., Kamel, S., & Khan, B. (2021). An efficient approach with application of linear and nonlinear models for evaluation of power transformer health index. IEEE Access, 9, 150172-150186.
  • Mohd Selva, A., Azis, N., Shariffudin. S., Ab Kadir, M. Z. A., Jasni, J., Yahaya, M. S., & Talib, M. A. (2021). Application of statistical distribution models to predict health index for condition-based management of transformers. Applied Sciences, 11(6), 2728.
  • Safi Khani, M., Pourhaji, M., Farmarez., F. (2002), The use of quality guarantees in raising production indicators in electrical industry equipment. The 19th International Electricity Conference. (in Persian).
  • Endrenyi, J., & Anders, G. J. (2006). Aging, maintenance, and reliability-approaches to preserving equipment health and extending equipment life. IEEE Power and Energy Magazine, 4(3), 59-67.
  • Chen, A., & Wu, G. S. (2007). Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging Markovian deterioration. International Journal of Production Research, 45(15), 3351-3379.
  • Ramezani, S., Moini, A., & Riahi, M. (2019). A Model to Determining the State of Degradation and Remaining Useful Life of Rotating Equipment, With a New Approach to Combination and Predicting Health Index. Modares Mechanical Engineering, 19(10), 2351-2365.
  • Zhang, C., Gupta, C., Farahat, A., Ristovski, K., & Ghosh, D. (2019). Equipment health indicator learning using deep reinforcement learning. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III 18 (pp. 488-504). Springer International Publishing.
  • Zarei Q., & Parsa-Mehr B. (2017). Identifying factors affecting the development of medical equipment exports using the data theory approach, 2017, Health and Treatment, (3) 9, 7-18. (in Persian).
  • Negri, E., Ardakani, H. D., Cattaneo, L., Singh, J., Macchi, M., & Lee, J. (2019). A digital twin-based scheduling framework including equipment health index and genetic algorithms. IFAC-PapersOnLine, 52(10), 43-48.
  • Peng, C., Tang, Z., Gui, W., Chen, Q., Zhang, L., Yuan, X., & Deng, X. (2020). Review of key technologies and progress in industrial equipment health management. IEEE Access, 8, 151764-151776.
  • Ramezani, S., Moini, A., & Riyahi, M. (2018). A model for determining the state of deterioration of rotating equipment and determining the remaining useful life, with a new approach to integrating and predicting the health index, Tarbiat Modares University Publications, (19) 10, 2351-2365. (in Persian).
  • Toothman, M., Braun, B., Bury, S. J., Dessauer, M., Henderson, K., Wright, R., ... & Barton, K. (2021, May). Trend-based repair quality assessment for industrial rotating equipment. In 2021 American Control Conference (ACC) (pp. 502-507). IEEE.
  • Naderian, A., Cress, S., Piercy, R., Wang, F., & Service, J. (2008, June). An approach to determine the health index of power transformers. In Conference Record of the 2008 IEEE International Symposium on Electrical Insulation (pp. 192-196). IEEE.
  • Vermeer, M., Wetzer, J., van der Wielen, P., de Haan, E., & de Meulemeester, E. (2015, June). Asset-management decision-support modeling, using a health and risk model. In 2015 IEEE Eindhoven PowerTech (pp. 1-6). IEEE.
  • DNO-Network Asset Indices Methodology Working Group. (2015). DNO Common Network Asset Indices Methodology.
  • Scatiggio, F., Rebolini, M., & Pompili, M. (2016). Health Index: The Last Frontier of TSO’s Asset Management. TERNA Rete, 1-9.
  • Crespo Márquez, A., de la Fuente Carmona, A., Guillén López, A. J., Rosique, A. S., Serra Parajes, J., Martínez-Galán Fernández, P., & Izquierdo, J. (2020). Defining asset health indicators (AHI) to support complex assets maintenance and replacement strategies. Value Based and Intelligent Asset Management in Industrial Plants and Infrastructures, 79-99.
  • Crespo Del Castillo, A., Sasidharan, M., Nentwich, C., Merino, J., & Kumar Parlikad, A. (2023). Data-Driven Asset Health Index–an application to evaluate Quay Cranes in container ports. Maritime Policy & Management, 1-19.

 

  • Chen, A., & Wu, G. S. (2007). Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging Markovian deterioration. International Journal of Production Research, 45(15), 3351-3379.
  • Subsonic Wind Tunnel. (2014) Aeronautics.nasa.gov.
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.