Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model

To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities an...

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Veröffentlicht in:	Applied sciences Jg. 14; H. 11; S. 4551
Hauptverfasser:	Fahmi, Al-Tekreeti Watban Khalid, Reza Kashyzadeh, Kazem, Ghorbani, Siamak
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2024
Schlagworte:	Accuracy Algorithms Alternative energy sources Capital costs Deep learning Efficiency Electric power-plants Electricity Electricity distribution Emission standards Emissions Energy consumption Energy resources Fault diagnosis gas turbine Gas-turbines Industrial plant emissions Lubricants & lubrication Machine learning Methods Natural gas Neural networks Operations management power plant Power plants predictive maintenance Renewable resources Sensors TCN–Autoencoder Turbines Wavelet transforms Iraq
ISSN:	2076-3417, 2076-3417
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Abstract	To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, this model introduces a new approach that performs anomaly detection with high accuracy. To train and test the proposed model, a bespoke dataset of CA 202 accelerometers installed in the Kirkuk power plant was used. The proposed model not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, and VAE models in terms of anomaly detection accuracy, but also shows the Mean Squared Error (MSE = 1.447), Root Mean Squared Error (RMSE = 1.193), and Mean Absolute Error (MAE = 0.712). These results confirm the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency in power plants.
AbstractList	To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, this model introduces a new approach that performs anomaly detection with high accuracy. To train and test the proposed model, a bespoke dataset of CA 202 accelerometers installed in the Kirkuk power plant was used. The proposed model not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, and VAE models in terms of anomaly detection accuracy, but also shows the Mean Squared Error (MSE = 1.447), Root Mean Squared Error (RMSE = 1.193), and Mean Absolute Error (MAE = 0.712). These results confirm the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency in power plants.
Audience	Academic
Author	Fahmi, Al-Tekreeti Watban Khalid Ghorbani, Siamak Reza Kashyzadeh, Kazem
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References	Qi (ref_41) 2020; 27 Tanaka (ref_2) 2019; 9 ref_35 ref_12 ref_10 ref_32 ref_31 Zhou (ref_23) 2020; 200 ref_19 Lim (ref_28) 2005; 127 ref_18 ref_39 Riley (ref_16) 1983; Volume 65 Arrigone (ref_29) 2005; 84 Fahmi (ref_34) 2024; 160 Babu (ref_26) 2016; 6 Poullikkas (ref_1) 2005; 9 Matthaiou (ref_14) 2017; 3 Zhong (ref_22) 2019; 137 Fahmi (ref_9) 2022; 134 Santoso (ref_30) 2022; 15 Zhu (ref_17) 2017; 109 ref_24 Mourad (ref_7) 2023; 146 Mylonas (ref_38) 2021; 24 ref_21 ref_20 Liu (ref_33) 2019; 1187 Das (ref_37) 2004; 32 Sun (ref_11) 2023; 75 Kashyzadeh (ref_40) 2023; 146 Volponi (ref_13) 2014; 136 ref_27 Ortega (ref_36) 2007; 2007 ref_8 Abram (ref_15) 2018; 64 ref_5 Tang (ref_25) 2024; 10 ref_4 Assareh (ref_3) 2023; 282 ref_6
References_xml	– volume: Volume 65 start-page: 627 year: 1983 ident: ref_16 article-title: Non-Destructive Evaluation of Ceramic Gas Turbine Components by X-Rays and Other Methods publication-title: Progress in Nitrogen Ceramics – ident: ref_18 doi: 10.1115/GT2015-42139 – volume: 146 start-page: 107107 year: 2023 ident: ref_7 article-title: Failure analysis of gas and wind turbine blades: A review publication-title: Eng. Fail. Ana doi: 10.1016/j.engfailanal.2023.107107 – volume: 136 start-page: 051201 year: 2014 ident: ref_13 article-title: Gas turbine engine health management: Past, present, and future trends publication-title: J. Eng. Gas. Turb Power doi: 10.1115/1.4026126 – volume: 2007 start-page: 1 year: 2007 ident: ref_36 article-title: Moving average and Savitzki-Golay smoothing filters using Mathcad publication-title: Pap. ICEE – volume: 6 start-page: 35 year: 2016 ident: ref_26 article-title: Framework for development of comprehensive diagnostic tool for fault detection and diagnosis of gas turbine engines publication-title: J. Aerosp. Qual. Reliab. (Spec. Issue) – volume: 64 start-page: 93 year: 2018 ident: ref_15 article-title: Temperature measurement techniques for gas and liquid flows using thermographic phosphor tracer particles publication-title: Prog. Energ. Combust. doi: 10.1016/j.pecs.2017.09.001 – ident: ref_32 doi: 10.1109/CyberneticsCom55287.2022.9865487 – volume: 109 start-page: 473 year: 2017 ident: ref_17 article-title: Lubricating oil conditioning sensors for online machine health monitoring–A review publication-title: Tribol. Int. doi: 10.1016/j.triboint.2017.01.015 – volume: 32 start-page: 818 year: 2004 ident: ref_37 article-title: Mean squared error of empirical predictor publication-title: Ann. Stat. doi: 10.1214/009053604000000201 – volume: 9 start-page: 389 year: 2019 ident: ref_2 article-title: Asserting the climate benefits of the coal-to-gas shift across temporal and spatial scales publication-title: Nat. Clim. Chang. doi: 10.1038/s41558-019-0457-1 – ident: ref_24 doi: 10.3390/app14083354 – volume: 15 start-page: 363 year: 2022 ident: ref_30 article-title: RNN-Autoencoder approach for anomaly detection in power plant predictive maintenance systems publication-title: Int. J. Intell. Eng. Syst. – ident: ref_35 – ident: ref_21 – volume: 282 start-page: 128423 year: 2023 ident: ref_3 article-title: A transient study on a solar-assisted combined gas power cycle for sustainable multi-generation in hot and cold climates: Case studies of Dubai and Toronto publication-title: Energy doi: 10.1016/j.energy.2023.128423 – volume: 137 start-page: 435 year: 2019 ident: ref_22 article-title: A novel gas turbine fault diagnosis method based on transfer learning with CNN publication-title: Measurement doi: 10.1016/j.measurement.2019.01.022 – volume: 127 start-page: 314 year: 2005 ident: ref_28 article-title: Diagnosis for loose blades in gas turbines using wavelet analysis publication-title: J. Eng. Gas. Turbines Power doi: 10.1115/1.1772406 – volume: 200 start-page: 117467 year: 2020 ident: ref_23 article-title: Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks publication-title: Energy doi: 10.1016/j.energy.2020.117467 – ident: ref_6 – ident: ref_5 doi: 10.1115/GT2009-59239 – ident: ref_4 – ident: ref_31 – volume: 1187 start-page: 042111 year: 2019 ident: ref_33 article-title: April. Anomaly detection for time series using temporal convolutional networks and Gaussian mixture model publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1187/4/042111 – ident: ref_27 – volume: 27 start-page: 1485 year: 2020 ident: ref_41 article-title: On mean absolute error for deep neural network based vector-to-vector regression publication-title: IEEE Signal Proc. Let. doi: 10.1109/LSP.2020.3016837 – ident: ref_10 – volume: 24 start-page: 1122 year: 2021 ident: ref_38 article-title: Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data publication-title: Wind. Energy doi: 10.1002/we.2621 – ident: ref_8 doi: 10.3390/ma16093303 – volume: 75 start-page: 36 year: 2023 ident: ref_11 article-title: Wear failure analysis of ball bearings based on lubricating oil for gas turbine publication-title: Ind. Lubr. Tribol. doi: 10.1108/ILT-04-2022-0151 – ident: ref_12 doi: 10.3390/aerospace6070083 – volume: 84 start-page: 1052 year: 2005 ident: ref_29 article-title: Theory and practice in using Fourier transform infrared spectroscopy to detect hydrocarbons in emissions from gas turbine engines publication-title: Fuel doi: 10.1016/j.fuel.2005.01.018 – volume: 146 start-page: 107128 year: 2023 ident: ref_40 article-title: New neural network-based algorithm for predicting fatigue life of aluminum alloys in terms of machining parameters publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2023.107128 – volume: 160 start-page: 108213 year: 2024 ident: ref_34 article-title: Fault detection in the gas turbine of the Kirkuk power plant: An anomaly detection approach using DLSTM-Autoencoder publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2024.108213 – volume: 10 start-page: 2233 year: 2024 ident: ref_25 article-title: Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method publication-title: Complex. Intell. Syst. doi: 10.1007/s40747-023-01268-0 – volume: 3 start-page: 54 year: 2017 ident: ref_14 article-title: Vibration monitoring of gas turbine engines: Machine-learning approaches and their challenges publication-title: Front. Built Environ. doi: 10.3389/fbuil.2017.00054 – volume: 134 start-page: 106094 year: 2022 ident: ref_9 article-title: A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants publication-title: Eng. Fail. Anal doi: 10.1016/j.engfailanal.2022.106094 – ident: ref_19 doi: 10.3390/s19030711 – ident: ref_20 – volume: 9 start-page: 409 year: 2005 ident: ref_1 article-title: An overview of current and future sustainable gas turbine technologies publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2004.05.009 – ident: ref_39 doi: 10.3390/buildings12040438
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SubjectTerms	Accuracy Algorithms Alternative energy sources Capital costs Deep learning Efficiency Electric power-plants Electricity Electricity distribution Emission standards Emissions Energy consumption Energy resources Fault diagnosis gas turbine Gas-turbines Industrial plant emissions Lubricants & lubrication Machine learning Methods Natural gas Neural networks Operations management power plant Power plants predictive maintenance Renewable resources Sensors TCN–Autoencoder Turbines Wavelet transforms
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Title	Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model
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