چکیده مقاله
This research deals with the problem of fault detection in gas turbineswhich are major sub systems of modern power generation equipment Therefore, the current research employs deep machine learning modelsknown as Temporal Convolutional Networks in combination with anAutoencoder aimed at improving anomaly detection accuracy andefficiency in gas turbines To this end, this work uses vibration datagathered from accelerometers in the Kirkuk gas power plant andcompares the efficiency of the proposed TCN Autoencoder model toconventional models such as GRU Autoencoder, LSTM Autoencoder,and Variational Autoencoder VAE Statistically, findings highlight thatthe TCN Autoencoder model performs better in terms of identifyingsubtle anomalies than the RW model and has lower error measurementsthat thus point to enhanced PM and operational effectiveness Theoutcomes from this research advance the ongoing endeavor of mergingmachine learning into machinery for precise and effective fault detection
کلیدواژهها
نویسندگان
Al-Tekreeti Watban Khalid Fahmi
Ph D Student, Department of Mechanical Engineering, Academy of Engineering, RUDN University,۶ Miklukho Maklaya Street, Moscow ۱۱۷۱۹۸, Russian Federation
Kazem Reza Kashyzadeh
Full Professor, Department of Transport Equipment and Technology, Academy of Engineering,RUDN University, ۶ Miklukho Maklaya Street, Moscow ۱۱۷۱۹۸, Russian Federation
Siamak Ghorbani
Associate Professor, Department of Mechanical Engineering, Academy of Engineering, RUDNUniversity, ۶ Miklukho Maklaya Street, Moscow ۱۱۷۱۹۸, Russian Federation
شیوه ارجاع
Watban Khalid Fahmi, Al-Tekreeti and Kashyzadeh, Kazem Reza and Ghorbani, Siamak,1403,Enhancing Fault Detection in Gas Turbines Using MachineLearning Models: A Case Study on Kirkuk Gas Power Plant,The 4th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
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مجموعه مقالات چهارمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات18 آذر 1403 · مشهد