Smart maintenance strategies in the combined cycle power plant

Fahmi, Al-Tekreeti Watban Khalid; Reza Kashyzadeh, Kazem; Ghorbani, Siamak

doi:10.22061/jcarme.2024.10797.2415

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	Smart maintenance strategies in the combined cycle power plant
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 22 خرداد 1403 اصل مقاله (804.43 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2024.10797.2415
نویسندگان
Al-Tekreeti Watban Khalid Fahmi¹؛ Kazem Reza Kashyzadeh^* ²؛ Siamak Ghorbani³
¹Ph.D. student, Department of Mechanical Engineering, Academy of Engineering, RUDN University, 6 Miklukho-Maklaya Street, Moscow 117198, Russian Federation
²Professor, Department of Transport Equipment and Technology, Academy of Engineering, RUDN University, Moscow, Russia.
³Associate Professor, Department of Mechanical Engineering, 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 (AS), Blade Tip Timing (BTT), Laser Doppler Vibrometer (LDV), and Strain Gauge (SG). Afterward, the data was pre-processed and used to train Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Random Forest Models (RFMs). Performance evaluation metrics, including accuracy, recall, F1-score, and Receiver Operating Characteristic (ROC), and Area Under Curve (AUC) were employed to assess the models, revealing varying degrees of success across combining techniques and models. Notable achievements 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

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Smart maintenance strategies in the combined cycle power plant