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مروری بر مفهوم تخمین عمر باقیمانده (RUL) در ماشین آلات دوار با بهرهگیری از رهیافت مدیریت سلامت پیشبینانه (PHM) | ||
مهندسی مکانیک دانشگاه تبریز | ||
مقاله 70، دوره 51، شماره 4 - شماره پیاپی 97، بهمن 1400، صفحه 627-635 اصل مقاله (399.56 K) | ||
نوع مقاله: مقاله مروری | ||
شناسه دیجیتال (DOI): 10.22034/jmeut.2022.11533 | ||
نویسندگان | ||
محمد ریاحی* 1؛ عمید مقصودی2 | ||
1استاد، دانشکده مهندسی مکانیک، دانشگاه علم و صنعت ایران، تهران، ایران | ||
2دانشجوی دکتری، دانشکده مهندسی مکانیک، دانشگاه علم و صنعت ایران، تهران، ایران | ||
چکیده | ||
نگهداری و تعمیرات از اساسیترین بخشهای یک صنعت به حساب میآید. در صنایعی که از ماشین آلات دوار استفاده میشود موضوع نگهداری و تعمیرات با اهمیت بیشتری پیگیری میشود. با داشتن یک الگوریتم مناسب برای نگهداری و تعمیرات میتوان از فجایع انسانی و مالی در اینگونه صنایع جلوگیری کرد. تا امروز روشهای متنوعی برای پیش بینی عمر مفید باقی مانده (RUL) در ماشین آلات دوار ارائه شدهاند، ولی جای خالی یک مقاله مروری که به خوبی بتواند روشهای مختلف را تفکیک کند و در مورد آن بحث کند، خالی است. در این مقاله مروری سعی بر این است تا پس از روشن سازی مفوم تخمین عمر مفید باقیمانده، سه روش متداول شامل روشهای مبتنی بر داده، روشهای مبتنی بر مدل و روشهای ادغامی به خوبی توضیح داده شود. سپس، در هر روش به مفهوم و ریاضیات RUL در ماشین آلات دوار پرداخته شود. در انتها نیز پیشنهادات ارزشمندی را برای پژوهشگران علاقهمند به تخمین عمر مفید باقیمانده و مدیریت سلامت پیشبینانه ارائه شده است. | ||
کلیدواژهها | ||
عمر مفید باقیمانده (RUL)؛ ماشین آلات دوار؛ مدیریت سلامت پیشبینانه (PHM)؛ روش مبتنی بر داده؛ روش مبتنی بر مدل؛ روش ادغامی | ||
مراجع | ||
[1] Kalgren P. W., Byington C. S., Roemer M. J., & Watson M. J., Defining PHM, a lexical evolution of maintenance and logistics. In 2006 IEEE autotestcon, pp. 353-358. IEEE, 2006. [2] Kim N. H., An D., & Choi J. H., Data-Driven Prognostics. In Prognostics and Health Management of Engineering Systems, pp. 179-241. Springer, Cham, 2017. [3] Riahi M, Shamekh H., Health monitoring of aboveground storage tanks’ floors: A new methodology based on practical experience. Russian Journal of Nondestructive Testing 42, No. 8, pp. 537-54, 2016. [4] ریاحی م. و مقصودی ع.، مروری بر روش نوین مدیریت سلامت پیش بینانه صنعتی و ماشین آلات: مفهوم، جایگاه، کاربردها و فرصت های آینده. مجله مهندسی نگهداری و مدیریت منابع، ش. 2، 1399. [5] Soualhi A, Hawwari Y, Medjaher K, Clerc G, Hubert R, Guillet F., PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes. International Journal of Prognostics and Health Management, No. 2, 2018. [6] R. Yam, P. Tse, L. Li, and P. Tu, Intelligent predictive decision support system for condition-based maintenance, The International Journal of Advanced Manufacturing Technology, vol. 17, pp. 383-391, 2001. [7] Heng A., Zhang S., Tan A.C., Mathew J., Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical systems and signal processing, vol. 23, pp. 724-739, 2009. [8] Soualhi A., Medjaher K., Zerhouni N., Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, vol. 64, pp. 52-62, 2014. [9] Ye Z.S., Chen N., Shen Y., A new class of Wiener process models for degradation analysis. Reliability Engineering & System Safety, vol. 139, pp. 58-67, 2015. [10] Lee J., Wu F., Zhao W., Ghaffari M., Liao L., Siegel D., Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, vol. 42, pp. 314-334, 2014. [11] Qian Y., Yan R., Gao R.X., A multi-time scale approach to remaining useful life prediction in rolling bearing. Mechanical Systems and Signal Processing, vol. 83, pp. 549-567, 2017. [12] Zhu S.P., Huang H.Z., Peng W., Wang H.K., Mahadevan S., Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliability Engineering & System Safety, vol. 146, pp. 1-12, 2016. [13] Riahi M., Ahmadi A., Comparison and analysis of two modern methods in the structural health monitoring techniques in aerospace. in Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, Vol. 9804, 2016. [14] Liao L., Köttig F., Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, Vol. 63, pp. 191-207, 2014. [15] Riahi M., and Maghsoudi A., Feature Selection in Milling Process Utilizing Wavelet Analysis,. in the 5th Biennial International Conference on Experimental Solid Mechanics (X-Mech),Iran University of Science and Technology, Tehran, 2020. [16] Riahi M., and Maghsoudi A., Identification of the Optimum Level of Wavelet Decomposition for Acoustic Emission Signal Denoising of a Milling Machine. in the 5th Biennial International Conference on Experimental Solid Mechanics (X-Mech), Iran University of Science and Technology, Tehran, 2020, [17] Lei Y., Intelligent fault diagnosis and remaining useful life prediction of rotating machinery. Butterworth-Heinemann Elsevier, United Kingdom, 2016. [18] Li N., Lei Y., Liu Z., Lin J., A particle filtering-based approach for remaining useful life predication of rolling element bearings. In 2014 International Conference on Prognostics and Health Managemen IEEEt, pp. 1-8., 2014. [19] Li X., Ding Q., Sun J.Q., Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, vol. 172, pp. 1-11, 2018. [20] Rai A., Upadhyay S.H., Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 232, pp. 1118-1, 2018. [21] Li X., Zhang W., Ding Q., Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering & System Safety, vol. 182, pp. 208-218, 2019. [22] Alaswad S., Xiang Y., A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety, vol. 157, pp. 54-63, 2017. [23] Keizer M.C., Flapper S.D., Teunter R.H., Condition-based maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research, vol. 261, pp. 405-420, 2017. [24] Lei Y., Li N., Guo L., Li N., Yan T., Lin J., Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, vol. 104, pp. 799-834, 2018. [25] Liu R., Yang B., Zio E., Chen X., Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, vol. 108, pp. 33-47, 2018. [26] Li N., Lei Y., Lin J., Ding S.X., An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, vol. 62, pp. 7762-7773, 2015. [27] Peng Y., Cheng J., Liu Y., Li X., Peng Z., An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings. Frontiers of Mechanical Engineering, vol. 13, pp. 301-310, 2018. [28] Ahadi E., Larky M., Riahi M., Applications of Artificial Intelligence on Prognostics of Rotating Machineries, In The 26th Annual International Conference of Iranian Society of Mechanical Engineers-ISME2018, School of Mechanical Engineering, Semnan University, Semnan, Iran, 2018. [29] Gebraeel N., Lawley M., Liu R., Parmeshwaran V., Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on industrial electronics, vol. 51, pp. 694-700, 2004. [30] Chen C., Zhang B., Vachtsevanos G., Orchard M., Machine condition prediction based on adaptive neuro–fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, vol. 58, pp. 4353-4364, 2010. [31] Liu J., Seraoui R., Vitelli V., Zio E., Nuclear power plant components condition monitoring by probabilistic support vector machine. Annals of Nuclear Energy, vol. 56, pp. 23-33, 2013. [32] Zio E., Di Maio F., Fatigue crack growth estimation by relevance vector machine. Expert Systems with Applications, vol. 39, pp. 10681-10692, 2012. [33] Cox DR., Regression models and life‐tables. Journal of the Royal Statistical Society: Series B (Methodological), vol. 34, pp. 187-202, 1972. [34] Zhao H., Li X., A cost sensitive decision tree algorithm based on weighted class distribution with batch deleting attribute mechanism. Information Sciences, vol. 378, pp. 303-316, 2017. [35] Park C., Padgett W.J., Accelerated degradation models for failure based on geometric Brownian motion and gamma processes. Lifetime Data Analysis, vol. 11, pp. 511-527, 2005. [36] Ye Z.S., On the conditional increments of degradation processes. Statistics & Probability Letters, vol. 83, pp. 2531-2536, 2013. [37] Pang C.K., Zhou J.H., Yan H.C., PDF and breakdown time prediction for unobservable wear using enhanced particle filters in precognitive maintenance. IEEE Transactions on Instrumentation and Measurement, vol. 64, pp. 649-659, 2014. [38] Park J.I., Bae S.J., Direct prediction methods on lifetime distribution of organic light-emitting diodes from accelerated degradation tests. IEEE Transactions on Reliability, vol. 59, pp. 74-90, 2010. [39] Si X.S., Wang W., Hu C.H., Chen M.Y., Zhou D.H., A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing, vol. 35, pp. 219-237, 2013 [40] دانش م.، محمدی ع. ا.، پایش وضعیت لقی در یک سرومکانیزم لنگ لغزشی با استفاده از تحلیل هارمونیکهای سیگنال حسگر انکودر، مجله مهندسی مکانیک دانشگاه تبریز, د 50، ش 1، ص 101-108، 1399. [41] س. حسنی فرد, ا. معماری, س. امینی, and م. خوشروان آذر, "بررسی معیارهای خستگی چند محوره در پیش بینی عمر استوانه توخالی از جنس آلیاژ GH4169 و مقایسه با نمونه مرجع تجربی," مهندسی مکانیک دانشگاه تبریز, د 48، ش 1، ص 123-132، 1397. [42] Orsagh R., Roemer M., Sheldon J., Klenke C.J., A comprehensive prognostics approach for predicting gas turbine engine bearing life. In Turbo Expo: Power for Land, Sea, and Air, vol. 41677, pp. 777-785. 2004. [43] Oppenheimer C.H., Loparo K.A., Physically based diagnosis and prognosis of cracked rotor shafts. in Component and Systems Diagnostics, Prognostics, and Health Management II, pp. 122-133, 2002. [44] Paris P., Erdogan F., A critical analysis of crack propagation laws, Journal of basic engineering, vol. 85, pp. 528-533, 1963. [45] Riahi M., Ansarifard M., Maintenance Improvement of Ball Bearings For Industrial Applications. International Journal of Industrial Engineering & Production Research, vol. 19, pp. 123-126, 2008. [46] طهماسبی و. ، قریشی م., و ذوالفقاری م.، ارائه یک مدل ریاضی، بررسی و بهینه سازی پارامتر های مؤثر در دمای فرآیند سوراخکاری استخوان کورتیکال، مجله مهندسی مکانیک دانشگاه تبریز، د 47، ش 1، ص 168-161، 1396. [47] Xia T., Dong Y., Xiao L., Du S., Pan E., Xi L., Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering & System Safety, vol. 178, pp. 255-268, 2018. [48] Zhang H., Kang R., Pecht M., A hybrid prognostics and health management approach for condition-based maintenance. in 2009 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1165-1169, 2009. [49] Du S., Lv J., Xi L., Degradation process prediction for rotational machinery based on hybrid intelligent model. Robotics and Computer-Integrated Manufacturing, vol. 28, pp. 190-207, 2012. [50] Xiao Q., Fang Y., Liu Q., Zhou S., Online machine health prognostics based on modified duration-dependent hidden semi-Markov model and high-order particle filtering. The International Journal of Advanced Manufacturing Technology, vol. 94, pp. 1283-1297, 2018. [51] Ramezani S., Moini A., Riahi M., 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, vol. 19, pp. 2351-2365, 2019. [52] Mazidi P., Bertling Tjernberg L., Sanz Bobi M.A., Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 231, pp. 121-129, 2017. [53] Pham H.T., Yang B.S., Nguyen T.T., Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing, vol. 32, pp. 320-330, 2012. [54] Wang Y., Peng Y., Zi Y., Jin X., Tsui K.L., A two-stage data-driven-based prognostic approach for bearing degradation problem. IEEE Transactions on Industrial Informatics, vol. 12, pp. 924-932, 2016. [55] Baptista M., Henriques E. M., de Medeiros I. P., Malere J. P., Nascimento C. L., Prendinger H., Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering, Reliability Engineering & System Safety, vol. 184, pp. 228-239, 2019. [56] Monitoring C., Diagnostics of machines-prognostics part 1: General guidelines. ISO13381-1:(e). vol. ISO/IEC Directives Part 2, IO f. S 14, 2004. | ||
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