پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 432 |
| تعداد مقالات | 5,614 |
| تعداد مشاهده مقاله | 7,355,400 |
| تعداد دریافت فایل اصل مقاله | 6,161,130 |
عیب‌یابی سازه جکتی فراساحلی با استفاده از پاسخ‌‌های دینامیکی بر پایه مدل شبیه‌سازی‌شده، حالت سالم مدل واقعی و شبکه عصبی خود رمزنگار عمیق | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 2، دوره 53، شماره 6 (Special Issue)، شهریور 1400، صفحه 3783-3808 اصل مقاله (3.69 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2020.18553.6843 | ||
| نویسندگان | ||
| زهره موسوی؛ سینا ورهرام؛ میر محمد اتفاق* ؛ مرتضی صادقی؛ سید ناصر رضوی | ||
| دانشگاه تبریز | ||
| چکیده | ||
| ازآنجایی‌که هزینه نگهداری و تعمیر سیستم‌های مکانیکی اعم از سازه‌ها و ماشین‌های دوار بسیار بالاست، یکی از راه‌های کاهش این هزینه‌ها، درنظرگرفتن تدابیری جهت بررسی عیوب در این سیستم‌ها قبل از هر نوع کار عملی است. در این مطالعه روش جدیدی برای عیب‌یابی سازه جکتی فراساحلی در حضور عدم قطعیت‌های مختلف مانند خطاهای مدل‌سازی، خطاهای اندازه‌گیری و نویزهای محیطی بر پایه مدل شبیه‌سازی‌شده و حالت سالم مدل واقعی ارائه‌شده است. در روش پیشنهادی، داده‌های سازه سالم واقعی برای به‌روزرسانی پارامترهای مدل شبیه‌سازی‌شده استفاده شده‌اند. برخی از بخش‌های سیگنال که مربوط به ذات سیستم نیستند، با استفاده از روش تجزیه مود تجربی دسته‌ای کامل حذف شده‌اند. داده‌های فرکانسی با استفاده از روش تجزیه حوزه فرکانس از سیگنال‌های ارتعاشی استخراج شده‌اند. یک شبکه عصبی خود رمزنگار عمیق، به‌منظور یادگیری ویژگی‌های حساس به عیب از داده‌های فرکانسی و عیب‌یابی سازه طراحی‌شده است. به‌منظور آموزش شبکه عمیق پیشنهادی، از داده‌های فرکانسی مدل شبیه‌سازی‌شده و حالت سالم مدل واقعی استفاده شده‌است. پس‌ازآن، داده‌های فرکانسی سازه واقعی برای ارزیابی شبکه عمیق پیشنهادی استفاده شده‌است. نتایج حاصله نشان می‌دهد که استفاده از روش پیشنهادی برای عیب‌یابی سازه جکتی فراساحلی صحت بالاتری نسبت به سایر روش‌های مقایسه‌ای دارد. | ||
| کلیدواژه‌ها | ||
| پایش وضعیت؛ سازه جکتی فراساحلی؛ به‌روزرسانی مدل؛ شبکه عصبی عمیق | ||
| عنوان مقاله [English] | ||
| Damage detection of offshore jacket structure using dynamic responses based on simulated model, intact state of real model and deep auto-encoder neural network | ||
| نویسندگان [English] | ||
| zohreh mousavi؛ sina varahram؛ Mir Mohammad Ettefagh؛ Morteza Sadeghi؛ seyed naser razavi | ||
| university of tabriz | ||
| چکیده [English] | ||
| Since the maintenance and repairing costs of mechanical systems, such as structures and rotating machines are significantly high, one way to reduce these costs is to consider some approaches before any operational work to check for damages in such systems. In this study, a new method is presented for damage detection of offshore jacket structures in the presence of various uncertainties, such as modeling errors, measurement errors and environmental noises, based on the simulated model and intact state of the real model. In the proposed method, real intact structure data is used to update the simulated model parameters. Some parts of the signal that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. Frequency data is extracted from the vibrational signals using the frequency domain decomposition method. A deep auto-encoder neural network is designed to learn the damage-sensitive features from the frequency data and to damage detection of the structure. In order to train the proposed deep network, frequency data of the simulated model and real intact state are used; then the frequency data of the real structure is used to test the proposed deep network. The results show that the proposed method is capable for damage detection of the offshore jacket structure with more accurate results than the other comparative methods. | ||
| کلیدواژه‌ها [English] | ||
| Condition monitoring, Offshore jacket structure, Model updating, Deep neural network | ||
| مراجع | ||
|
[1] S. Cofre-Martel, P. Kobrich, E. Lopez Droguett, V. Meruane, Deep convolutional neural network-based structural damage localization and quantification using transmissibility data, Shock and Vibration, 2019 (2019). [2] X. Wang, J. Jiao, J. Yin, W. Zhao, X. Han, B. Sun, Underwater sonar image classification using adaptive weights convolutional neural network, Applied Acoustics, 146 (2019) 145-154. [3] Y. Bao, Z. Tang, H. Li, Y. Zhang, Computer vision and deep learning–based data anomaly detection method for structural health monitoring, Structural Health Monitoring, 18(2) (2019) 401-421. [4] H. Ahmed, M.L.D. Wong, A.K. Nandi, Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features, Mechanical Systems and Signal Processing, 99 (2018) 459-477. [5] R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, R.X. Gao, Deep learning and its applications to machine health monitoring, Mechanical Systems and Signal Processing, 115 (2019) 213-237. [6] W. Zhang, C. Li, G. Peng, Y. Chen, Z. Zhang, A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load, Mechanical Systems and Signal Processing, 100 (2018) 439-453. [7] L. Jing, M. Zhao, P. Li, X. Xu, A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox, Measurement, 111 (2017) 1-10. [8] Y. Chen, G. Peng, C. Xie, W. Zhang, C. Li, S. Liu, ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis, Neurocomputing, 294 (2018) 61-71. [9] J. Guo, J. Wu, J. Guo, Z. Jiang, A damage identification approach for offshore jacket platforms using partial modal results and artificial neural networks, Applied Sciences, 8(11) (2018) 2173. [10] K. Liu, R.-J. Yan, C.G. Soares, Damage identification in offshore jacket structures based on modal flexibility, Ocean Engineering, 170 (2018) 171-185. [11] A. Mojtahedi, M.L. Yaghin, Y. Hassanzadeh, M. Ettefagh, M. Aminfar, A. Aghdam, Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system, Applied Ocean Research, 33(4) (2011) 398-411. [12] Z. Ding, J. Li, H. Hao, Z.-R. Lu, Structural damage identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm, Engineering Structures, 185 (2019) 301-314. [13] M. Fallahian, F. Khoshnoudian, V. Meruane, Ensemble classification method for structural damage assessment under varying temperature, Structural Health Monitoring, 17(4) (2018) 747-762. [14] S. Teng, G. Chen, G. Liu, J. Lv, F. Cui, Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks, Applied Sciences, 9(16) (2019) 3376. [15] S. Varahram, P. Jalali, M.H. Sadeghi, S. Lotfan, Experimental Study on the Effect of Excitation Type on the Output-Only Modal Analysis Results, Transactions of FAMENA, 43(3) (2019) 37-52. [16] M.E. Torres, M.A. Colominas, G. Schlotthauer, P. Flandrin, A complete ensemble empirical mode decomposition with adaptive noise, in: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 2011, pp. 4144-4147. [17] J.-H. Yi, J.-S. Park, S.-H. Han, K.-S. Lee, Modal identification of a jacket-type offshore structure using dynamic tilt responses and investigation of tidal effects on modal properties, Engineering Structures, 49 (2013) 767-781. [18] S.-L. Hung, H. Adeli, Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer, Neurocomputing, 5(6) (1993) 287-302. [19] I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT press, 2016. [20] U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, H. Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in biology and medicine, 100 (2018) 270-278. [21] E. Barton, C. Middleton, K. Koo, L. Crocker, J. Brownjohn, Structural finite element model updating using vibration tests and modal analysis for NPL Footbridge–SHM demonstrator, in: Journal of Physics: Conference Series, IOP Publishing, 2011, pp. 012105. [22] Z. Mousavi, M.M. Ettefagh, M.H. Sadeghi, S.N. Razavi, Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state, Applied Acoustics, 168 (2020) 107402. [23] Z. Mousavi, T.Y. Rezaii, S. Sheykhivand, A. Farzamnia, S. Razavi, Deep convolutional neural network for classification of sleep stages from single-channel EEG signals, Journal of neuroscience methods, 324 (2019) 108312. [24] M. Li, H. Wang, G. Tang, H. Yuan, Y. Yang, An improved method based on CEEMD for fault diagnosis of rolling bearing, Advances in Mechanical Engineering, 6 (2014) 676205. [25] M. Kuai, G. Cheng, Y. Pang, Y. Li, Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS, Sensors, 18(3) (2018) 782. [26] X. Zhang, Y. Liang, J. Zhou, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM, Measurement, 69 (2015) 164-179. [27] D. Formenti, M. Richardson, Parameter estimation from frequency response measurements using rational fraction polynomials (twenty years of progress), in: Proceedings of International Modal Analysis Conference XX, Citeseer, 2002. [28] B. Song, D. Casem, J. Kimberley, Dynamic Behavior of Materials, Volume 1: Proceedings of the 2013 Annual Conference on Experimental and Applied Mechanics, Springer Science & Business Media, 2013. [29] G. Deodatis, B.R. Ellingwood, D.M. Frangopol, Safety, reliability, risk and life-cycle performance of structures and infrastructures, CRC Press, 2014. [30] L. Meirovitch, Analytical methods in vibrations, (1967). [31] C. Rajakumar, C. Rogers, The Lanczos algorithm applied to unsymmetric generalized eigenvalue problem, International Journal for Numerical Methods in Engineering, 32(5) (1991) 1009-1026. [32] H.D. Beale, H.B. Demuth, M. Hagan, Neural network design, Pws, Boston, (1996). [33] S. Sheykhivand, T.Y. Rezaii, A. Farzamnia, M. Vazifehkhahi, Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier, in: 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), IEEE, 2018, pp. 1-6. [34] Z. Mousavi, M. M. Ettefagh, S. M. H. Sadeghi, S. N Razavi, Identification and Damage Detection of beam-like structure Using Vibration Signals Based on Simulated Model, Real Healthy State and Deep Convolutional Neural Network, AUT Journal of Mechanical Engineering, 2020, (in Persian). [35] S. Kim, J.-H. Choi, Convolutional neural network for gear fault diagnosis based on signal segmentation approach, Structural Health Monitoring, 18(5-6) (2019) 1401-1415.
| ||
|
آمار تعداد مشاهده مقاله: 1,032 تعداد دریافت فایل اصل مقاله: 1,096 |
||