Smart maintenance strategies in combined cycle power plant

Fahmi, A. W. K.; Reza Kashyzadeh, K.; Ghorbani, S.

doi:10.22061/jcarme.2024.10797.2415

تعداد نشریات	13
تعداد شماره‌ها	237
تعداد مقالات	2,406
تعداد مشاهده مقاله	3,888,040
تعداد دریافت فایل اصل مقاله	2,826,464

	Smart maintenance strategies in combined cycle power plant
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 3، دوره 14، شماره 1 - شماره پیاپی 27، مهر 2024، صفحه 35-46 اصل مقاله (704.95 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2024.10797.2415
نویسندگان
A. W. K. Fahmi¹؛ K. Reza Kashyzadeh^* ²؛ S. Ghorbani¹
¹Department of Mechanical Engineering, Academy of Engineering, RUDN University, 6 Miklukho-Maklaya Street, Moscow 117198, Russian Federation
²Department of Transport Equipment and Technology, Academy of Engineering, RUDN University, 6 Miklukho-Maklaya Street, Moscow 117198, Russian Federation
تاریخ دریافت: 14 فروردین 1403، تاریخ بازنگری: 21 خرداد 1403، تاریخ پذیرش: 22 خرداد 1403
چکیده
This research investigates the effectiveness of various vibration data acquisition techniques coupled with different machine learning models for detecting anomalies and classifying them. To this end, synthetic vibration data was generated for techniques such as eddy current proximity transducers (ECPT), accelerometer sensor, blade tip timing, laser doppler vibrometer (LDV), and strain gauge. Afterward, the data was pre-processed and used to train gradient boosting machine, support vector machine, and random forest models. Performance evaluation metrics, including accuracy, recall, F1-score, receiver operating characteristic, and area Under curve were employed to assess the models, revealing varying degrees of success across combining techniques and models. Notable achievements were observed for the random forest model coupled with the eddy current proximity transducers technique, underscoring the significance of informed technical selection and model optimization in enhancing vibration anomaly detection systems in combined cycle power plants. The results showed that the LDV technique has a significant increase in accuracy from about 0.49 to approximately 0.52, while the ECPT technique has improved from about 0.9 to close 1.0. These advances highlight the growing accuracy of the methods and enable the development of more efficient and reliable learning machines.
کلیدواژه‌ها
Anomaly detection؛ Machine learning؛ Eddy current proximity transducers؛ Blade tip timing؛ Laser doppler vibrometer

مراجع
[1] A. T. W. K. Fahmi, K. R. Kashyzadeh and S. Ghorbani, “Fault detection in the gas turbine of the Kirkuk power plant: an anomaly detection approach using DLSTM-autoencoder”, Eng. Fail. Anal., Vol. 160, pp. 108213, (2024). [2] W. Fu and W. S. Hopkins, “Applying machine learning to vibrational spectroscopy”, J. Phys. Chem. A, Vol. 122, No 1, pp. 167–171, (2018). [3] J. A. B. Villanueva, F. J. J. E. Aguilar, E. C. Trujillo, R. C. R. Rez and M. T. Garcia, “Analysis of steam turbine instabilities of a 100 mw combined cycle power plant”, Conf. proc. Int. Mech. Eng. Cong. Exp., Vancouver, British Columbia, Canada, Vol. 44458, pp. 11–19, (2010). [4] M. N. Khan and I. Tlili, “New advancement of high performance for a combined cycle power plant: Thermodynamic analysis”, Case Stud. Therm. Eng., Vol. 12, pp. 166-175, (2018). [5] A. T. W. K. Fahmi, K. R. Kashyzadeh and S. Ghorbani, “A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants”, Eng. Fail. Anal., Vol. 134, pp. 106094,‏ (2022). [6] H. Lai and T. A. Adams II, “Life cycle analyses of SOFC/gas turbine hybrid power plants accounting for long-term degradation effects”, J. Clean Prod., Vol. 412, pp. 137411, (2023). [7] G. N. D. S. Sudhakar and A. S. Sekhar, “Coupling misalignment in rotating machines: modelling, effects, and monitoring”, Noise Vib. Worldw., Vol. 40, pp. 17–39, (2009). [8] Y. Zhang, X. Wu, Y. Lei, J. Cao and W. H. Liao, “Self-powered wireless condition monitoring for rotating machinery”, IEEE IOT-J., Vol. 11, Issue. 2, pp. 3095–3107, (2023). [9] H. P. Bloch, Less costly turbo equipment uprates through optimized coupling selection, Texas A&M University, Gas Turbine Laboratories, (1975). [10] J. K. Sinha, W. Hahn, K. Elbhbah, G. Tasker and I. Ullah, “Vibration investigation for low pressure turbine last stage blade failure in steam turbines of a power plant”, Conf. proc. Turbine Tech. Conf. Exp., Copenhagen, Denmark, Vol. 44731, pp. 363–371, (2012). [11] S. Edwards, A. W. Lees and M. I. Friswell, “The influence of torsion on rotor/stator contact in rotating machinery”, JSV, Vol. 225, No 4, pp. 767–778, (1999). [12] F. F. Ehrich, “Self-excited vibration”, Shock and Vibration Handbook, 5^th ed., Eds. A. G. Pierso and T. L. Paez, McGraw-Hill, New York, pp. 171–195 (1976). [13] C. Wang, D. Zhang, Y. Ma, Z. Liang and J. Hong, “Theoretical and experimental investigation on the sudden unbalance and rub-impact in rotor system caused by blade off”, Mech. Syst. Signal Pr., Vol. 76–77, pp. 111–135, (2016). [14] Y. J. Lee and Y. H. Ju, “An assessment of insulation condition for generator rotor windings”, Int. Conf. Cond. Monit. Diag., China, Beijing, pp. 543–545, (2008). [15] J. Kapler, S. Campbell and M. Credland, “Continuous automated flux monitoring for turbine generator rotor condition assessment”, EPRI Workshop Charlotte, NC, Toronto, Canada, pp. 1–15, (2004). [16] W. Peng and L. Yingzheng, “Unsteady flow behavior of a steam turbine control valve in the choked condition: field measurement, detached eddy simulation and acoustic modal analysis”, Appl. Therm. Eng., Vol. 117, pp. 725–739, (2017). [17] H. Wang, B. Ju, W. Li and Z. Feng, “Ultrastable eddy current displacement sensor working in harsh temperature environments with comprehensive self-temperature compensation”, Sensor Actuat. A-Phys., Vol. 211, 98–104, (2014). [18] K. S. Wanga, D. Guo and P. S. Heyns, “The application of order tracking for vibration analysis of a varying speed rotor with a propagating transverse crack”, Eng. Fail. Anal., Vol. 211, pp. 91–101, (2012). [19] S. Hanly, “Accelerometer specifications: deciphering an accelerometer’s datasheet”. [20] Machine learning random forest algorithm - javatpoint. (n.d.). [21] F. Mevissen and M. Meo, “A review of NDT/structural health monitoring techniques for hot gas components in gas turbines”, Sensors, Vol. 19, No 3, pp. 711, (2019). [22] Most common myths about accelerometers and frequency range adash. (n.d.). [23] J. Zhang, F. Duan, G. Niu, J. Jiang, and J. Li, “A blade tip timing method based on a microwave sensor”, Sensors, Vol. 17, No. 5, pp. 1097, (2017).‏ [24] M. Zielinski and G. Ziller, “Noncontact vibration measurements on compressor rotor blades”, Meas. Sci. Technol., Vol. 11, pp. 847–847, (2000). [25] M. Schewe and C. Rembe, “Signal diversity for laser-doppler vibrometers with raw-signal combination”, Sensors, Vol. 21, No. 3, pp. 998–998, (2021). [26] L. V. Anand, D. Hepsiba, S. Palaniappan, B. Sumathy, P. Vijayakumar and S. S. Rani, “Automatic strain sensing measurement on steel beam using strain gauge”, Mater Today-Proc, Vol. 45, pp. 2578–2580, (2021). [27] K. R. Kashyzadeh and S. Ghorbani, “New neural network-based algorithm for predicting fatigue life of aluminum alloys in terms of machining parameters”, Eng. Fail. Anal., Vol. 146, pp. 107128, (2023). [28] E. Maleki, O. Unal, S. S. M. Sahebari and K. R. Kashyzadeh, “A novel approach for analyzing the effects of almen intensity on the residual stress and hardness of shot-peened (TiB+TiC)/Ti–6Al–4V composite: Deep learning”, Materials, Vol. 16, No. 13, pp. 4693.‏ (2023). [29] K. R. Kashyzadeh, N. Amiri, S. Ghorbani and K. Souri, “Prediction of concrete compressive strength using a back-propagation neural network optimized by a genetic algorithm and response surface analysis considering the appearance of aggregates and curing conditions”, Buildings, Vol. 12, No. 4, pp. 438, (2022). [30] Laser doppler vibrometry: fundamentals-Polytec. [31] Characteristics of a strain gauge sensor - Bestech Australia. Bestech Australia. [32] D. Vyroubal, “Eddy-current displacement transducer with extended linear range and automatic tuning”, IEEE T. Instrum. Meas., Vol. 58, No. 9, pp. 3221–3231, (2009).
آمار تعداد مشاهده مقاله: 991 تعداد دریافت فایل اصل مقاله: 828

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Smart maintenance strategies in combined cycle power plant