بازشناخت الگوی نیروهای یاتاقانی محور چرخان صلب دارای نامیزانی‌های جرمی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیآت علمی-دانشکده مهندسی مکانیک-دانشگاه صنعتی خواجه نصیرالدین طوسی

2 دانشکده مهندسی مکانیک-دانشگاه صنعتی خواجه نصیرالدین طوسی-تهران

چکیده

در سیستم‌های چرخان صنعتی به دلایل مختلف مانند نا‌همراستایی یاتاقانی و وجود نامیزانی‌های جرمی، نیرو‌های مختلفی در یاتاقان تولید می‌شود. روش‌های بازشناخت الگوهای نامیزانی‌های جرمی زیان‌ آور تاکنون بر اساس استفاده از سنسورهای اندازه‌گیری جابجایی و یا سرعت طراحی و تدوین شده‌اند. در کنار بار محاسباتی بالای روش‌های مذکور، پهنای باند نسبتاً کم و قیمت بالای تجهیزات مورد نیاز این نوع روش‌ها، تا کنون فناوری‌های لازم بر اساس تکنیک‌های تحلیل بر مبنای اندازه‌گیری شتاب یاتاقان‌ها توسعه داده نشده است. ابتدا، معادلات حرکت و نیروهای موجود در یاتاقان‌های غیر منعطف صلب و کاملاً همراستا برای روتوری نامیزان با 15 جرم نامیزان موجود در سه صفحه با فواصل متفاوت استخراج شده‌اند. سپس، با حل عددی معادلات، الگوهای نیرویی یاتاقان‌ها حاصل شده و تأثیر هر کدام از متغیرهای دینامیکی روتور برای دو جرم نامیزان مورد تحلیل قرار می‌گیرند. نوآوری‌های مقاله‌ی حاضر به این شرح گزارش می‌گردند: با استفاده از یک شبکه‌ی عصبی ثابت شد که برای عیب‌یابی سیستم تنها دو سنسور شتاب‌سنج کافی است. در ادامه دو شبکه‌ی عصبی عمیق با 5 لایه طراحی شد که داده‌های ورودی، سرعت روتور، اندازه‌ی اقطار و میزان اوریب بودن بیضی بوده، و داده‌های خروجی برای شبکه‌ی اول تمامی متغیرهای لازم اجرام نامیزان شامل فاصله صفحه چرخش در روتور، زاویه، جرم و شعاع ذره و در شبکه‌ی دوم اختلاف زاویه‌ی اجرام، فاصله‌ی بین دو صفحه، فاصله‌ی یاتاقان  A از وسط دو صفحه، و حاصل‌ضرب جرم و شعاع هر کدام از نامیزانی‌ها بوده که دقت نهایی بدست آمده 95 درصد گزارش شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Pattern Recognition of Unbalanced Rigid Rotor Bearing Forces

نویسندگان [English]

  • Mohammad Reza Homaeinezhad 1
  • Mohammad Hosein Saeidi Mostaghim 2
  • Farnood Arab 2
1 K. N. Toosi University Of Technology, No. 17, Pardis Street, Mollasadra Ave., Vanak Square, Tehran, Iran. P.O Box: 19395-1999, Postal Code: 19991-43344.Tel: (+98 21) 84063284, Mobile: (+98) 9121899445, Fax: (+98 21) 88677274.
2 K. N. Toosi University Of Technology, No. 17, Pardis Street, Mollasadra Ave., Vanak Square, Tehran, Iran. P.O Box: 19395-1999, Postal Code: 19991-43344.Tel: (+98 21) 84063284, Mobile: (+98) 9121899445, Fax: (+98 21) 88677274.
چکیده [English]

In industrial rotatory machines, different forces in rotor bearings are generated due to various impaired mechanical sources, namely bearing misalignment and nonhomogeneous mass distribution (unbalance). By precisely analyzing and diagnosing the produced patterns of bearing forces, one can determine the unbalance parameters such as quantities of masses, their distance from the rotational axis, and characteristics of corresponding parallel planes. Consequently, it will be possible to formulate pragmatic protocols according to which the maintenance engineers of rotatory systems will pinpoint properties of problematic imbalance masses and then straightforwardly balance them. In the procedure of conducting this research, several exemplary imbalance masses are deployed on a rotatory mechanical shaft and the equations of motion and forces in perfectly aligned rigid bearings are extracted. Then, by applying a neural network-oriented system the patterns of bearing forces are recognized and the characteristics of the nominal masses including magnitudes, distances from the rotational axis, angles as well as the unbalance type are determined. The accuracy of predicting 8 variables of balancing masses was 41% and after eliminating the redundant overlaps from principal components, the accuracy of predicted 5 variables of balancing masses significantly increased to 95%. Also, by implementing another comprehensive neural network system, it was shown that by exerting two separate balancing masses, the applicability of this method in balancing any faulty systems with dynamic unbalance is possible.

کلیدواژه‌ها [English]

  • Unbalanced mass
  • Rigid rotor
  • Rotor mechanical defects
  • Force patterns
  • Artificial intelligence

مراجع

[1] C. Scheffer, P. Girdhar, Practical machinery vibration analysis and predictive maintenance, Elsevier, 2004.
[2] M.L. Adams, Rotating machinery vibration: from analysis to troubleshooting, CRC Press, 2000.
[3] G.A. Correa JC, Mechanical Vibrations and Condition Monitoring, Academic Press, 2020.
[4] R.B. Randall, Vibration-based condition monitoring: industrial, automotive and aerospace applications, John Wiley & Sons, 2021.
[5] O. Mey, W. Neudeck, A. Schneider, O. Enge-Rosenblatt, Machine learning-based unbalance detection of a rotating shaft using vibration data, in:  2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2020, pp. 1610-1617.
[6] M. Gohari, A. Kord, Unbalance rotor parameters detection based on artificial neural network, Int J Acoust Vib, 24(1) (2019) 113-118.
[7] C.E. Rodrigues, C.L.N. Júnior, D.A. Rade, Machine learning techniques for fault diagnosis of rotating machines using spectrum image of vibration orbits, in:  Congresso Brasileiro de Automática-CBA, 2020.
[8] H. Yadav, S.H. Upadhyay, S.P. Harsha, Study of effect of unbalanced forces for high speed rotor, Procedia Engineering, 64 (2013) 593-602.
[9] T. Choudhury, R. Viitala, E. Kurvinen, R. Viitala, J. Sopanen, Unbalance estimation for a large flexible rotor using force and displacement minimization, Machines, 8(3) (2020) 39.
[10] P. Kumar, R. Tiwari, Dynamic analysis and identification of unbalance and misalignment in a rigid rotor with two offset discs levitated by active magnetic bearings: A novel trial misalignment approach, Propulsion and Power Research, 10(1) (2021) 58-82.
[11] G. Adiletta, A. Guido, C. Rossi, Nonlinear dynamics of a rigid unbalanced rotor in journal bearings. Part I: theoretical analysis, Nonlinear Dynamics, 14(1) (1997) 57-87.
[12] V. Fedák, P. Záskalický, Z. Gelvanič, Analysis of balancing of unbalanced rotors and long shafts using GUI MATLAB, in:  MATLAB Applications for the Practical Engineer, IntechOpen, 2014.
[13] S.-h. Wang, W. Guo, X.-y. Xu, Y.-f. Liu, W.-y. Li, Modeling unbalanced rotor system with continuous viscoelastic shaft by frequency-dependent shape function, Journal of Central South University, 20(12) (2013) 3421-3430.
[14] S. Sendhilkumar, N. Mohanasundaram, M. Senthilkumar, S. Sivanandam, Elman neural network for diagnosis of unbalance in a rotor-bearing system, International Journal of Mechanical and Mechatronics Engineering, 10(3) (2016) 613-617.
[15] S. Paudyal, Classification of Rotating Machinery Fault Using Vibration Signal, The University of North Dakota, 2019.
[16] F.L. Santos, M.L.M. Duarte, M.T.C. de Faria, A.C. Eduardo, Balancing of a rigid rotor using artificial neural network to predict the correction masses, Acta Scientiarum. Technology, 31(2) (2009) 151-157.
[17] M.A. Saleem, G. Diwakar, M. Satyanarayana, Detection of unbalance in rotating machines using shaft deflection measurement during its operation, IOSR J. Mech. Civ. Eng, 3(3) (2012) 08-20.
[18] S. Ahamed, M. Mitra, S. Sengupta, A. Sarkar, Identification of Mass-Unbalance in Rotor of an Induction Motor Through Envelope Analysis of Motor Starting Current at no Load, Journal of Engineering Science & Technology Review, 5(1) (2012).
[19] M.M. Tahir, A. Hussain, S. Badshah, A.Q. Khan, N. Iqbal, Classification of unbalance and misalignment faults in rotor using multi-axis time domain features, in:  2016 International Conference on Emerging Technologies (ICET), IEEE, 2016, pp. 1-4.
[20] H. Jeong, S. Park, S. Woo, S. Lee, Rotating machinery diagnostics using deep learning on orbit plot images, Procedia Manufacturing, 5 (2016) 1107-1118.
[21] P. Pennacchi, A. Vania, Diagnosis and model based identification of a coupling misalignment, Shock and Vibration, 12(4) (2005) 293-308.
[22] N. Bachschmid, P. Pennacchi, A. Vania, Diagnostic significance of orbit shape analysis and its application to improve machine fault detection, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 26 (2004) 200-208.
[23] S. Zhao, X. Ren, Q. Zheng, K. Lu, C. Fu, Y. Yang, Transient dynamic balancing of the rotor system with uncertainty, Mechanical Systems and Signal Processing, 171 (2022) 108894.
[24] F. Liu, C. Xiang, H. Liu, X. Chen, F. Feng, H. Cong, K. Yu, Model and experimental verification of a four degrees-of-freedom rotor considering combined eccentricity and electromagnetic effects, Mechanical Systems and Signal Processing, 169 (2022) 108740.
[25] Z. Jian, W. Huachun, W. Weiyu, Y. Kezhen, H. Yefa, G. Xinhua, S. Chunsheng, Online unbalance compensation of a maglev rotor with two active magnetic bearings based on the LMS algorithm and the influence coefficient method, Mechanical Systems and Signal Processing, 166 (2022) 108460.
[26] L. Zhao, H. Zhang, P. Shen, Y. Liu, Nonlinear Dynamic Characteristics of Rod Fastening Rotor with Preload Relaxation, Energies, 15(3) (2022) 1052.
[27] B. Nayek, A. Das, J. Dutt, Model based estimation of inertial parameters of a rigid rotor having dynamic unbalance on Active Magnetic Bearings in presence of noise, Applied Mathematical Modelling, 97 (2021) 701-720.
[28] L. Li, Z. Luo, F. He, K. Sun, X. Yan, Experimental and numerical investigations on an unbalance identification method for full-size rotor system based on scaled model, Journal of Sound and Vibration, 527 (2022) 116868.
[29] R. Tiwari, P. Kumar, An innovative virtual trial misalignment approach for identification of unbalance, sensor and active magnetic bearing misalignment along with its stiffness parameters in a magnetically levitated flexible rotor system, Mechanical Systems and Signal Processing, 167 (2022) 108540.
[30] E. Iseli, J. Schiffmann, Experimental and numerical investigation of the unbalance behavior of rigid rotors supported by spiral-grooved gas journal bearings, Mechanical Systems and Signal Processing, 174 (2022) 109080.
[31] Y. Zhang, W. Wang, D. Wei, G. Wang, J. Xu, K. Liu, Dynamic stability of unbalance-induced vibration in a turbocharger rotor-bearing system with the nonlinear effect of thermal turbulent lubricating fluid film, Journal of Sound and Vibration, 528 (2022) 116909.
[32] Z. Zheng, J. Lin, Y. Hu, Q. Zhou, C. Yi, Dynamic unbalance identification and quantitative diagnosis of cardan shaft in high-speed train based on improved TQWT-RBFNN-NSGA-II method, Engineering Failure Analysis, 136 (2022) 106226.
[33] K.F. Riley, M.P. Hobson, S.J. Bence, Mathematical methods for physics and engineering, in, American Association of Physics Teachers, 1999.
[34] S.M. Ibn Shamsah, J.K. Sinha, Rotor unbalance estimation with reduced number of sensors, Machines, 4(4) (2016) 19.
 
نشریه مهندسی مکانیک امیرکبیر
دوره 54، شماره 6
شهریور 1401
صفحه 1249-1270

فایل ها

هم رسانی

ارجاع به این مقاله

آمار