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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Bibliographic Details
Published in:Applied sciences Vol. 14; no. 11; p. 4551
Main Authors: Fahmi, Al-Tekreeti Watban Khalid, Reza Kashyzadeh, Kazem, Ghorbani, Siamak
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2024
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app14114551