Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning

Hong, Chang Woo; Kim, Jeongju

doi:10.1007/s42835-023-01488-x

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…To a certain extent, it can characterize the operating condition of the MDE and the load distribution of each cylinder [8]. Different degrees of variation in the EGT can reflect faults in different subsystems of the MDE, and the temperature changes relatively slowly with minimal interference from external factors [9]. Real-time monitoring and prediction of the EGT can provide insight into the health status of MDE, ensuring the normal operation of ships [10].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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As the core component of a ship’s engine room, the operation of a marine diesel engine (MDE) directly affects the economy and safety of the entire vessel. Predicting the future changes in the status parameters of a MDE helps to understand the operational status, enabling timely warnings to the engine crew, and to ensure the safe navigation of the vessel. Therefore, this paper combines the temporal pattern attention mechanism with the bidirectional long short-term memory (BiLSTM) network to propose a novel trend prediction method for short-term exhaust gas temperature (EGT) forecasting. First, the Pearson correlation analysis (PCA) is conducted to identify input feature variables that are strongly correlated with the EGT. Next, the BiLSTM network models input feature variables such as load, fuel oil pressure, and scavenging air pressure and capture the interrelationships between different vectors from the hidden layer matrix within the BiLSTM network. This allows the selection of valuable information across different time steps. Meanwhile, the temporal pattern attention (TPA) mechanism has the ability to explore complex nonlinear dependencies between different time steps and series. This assigns appropriate weights to the feature variables within different time steps of the BiLSTM hidden layer, thereby influencing the input effect. Finally, the improved slime mold algorithm (ISMA) is utilized to optimize the hyperparameters of the prediction model to achieve the best level of short-term EGT trend prediction performance based on the ISMA-BiLSTM-TPA model. The prediction results show that the mean square error, the mean absolute percentage error, the root mean square error and the coefficient of determination of the model are 0.4284, 0.1076, 0.6545 and 98.2%, respectively. These values are significantly better than those of other prediction methods, thus fully validating the stability and accuracy of the model proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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“…Lastly, recent studies by Hong and Kim (2023); De Castro-Cros et al (2021) employed machine learning techniques to predict gas turbine engine performance. Hong and Kim (2023) utilized operational parameters like fuel flow and compressor discharge pressure, employing artificial neural networks and support vector regression algorithms. Their approach accurately predicted the gas turbine's performance.…”

Section: Introductionmentioning

confidence: 99%

“…Their approach accurately predicted the gas turbine's performance. Similarly, Hong and Kim (2023); De Castro-Cros et al (2021) developed an artificial neural network model using data from a heavy-duty gas turbine unit, incorporating various operational parameters. The resulting model successfully predicted the gas turbine engine's performance.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Fadja,

Cota,

Bertasi

et al. 2024

Journal of Computer Science

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Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.

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“…Due to their potential to extract representative features and perform data mining and their exclusive reliance on the historical data of a system, these methods are highly suitable for practical applications. Data-driven methods can be divided into the statistical methods of the past and more recent techniques such as machine learning and deep learning [29]. Traditional statistical methods include Autoregression [30], Kalman filtering [31], and Bayesian networks [32].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, hybrid models, which combine the advantages of multiple models, exhibit superior generalization capabilities compared to single models. For instance, Hong et al [29] combined CNN and RNN algorithms to construct a hybrid deep learning model for predicting the exhaust gas temperatures of gas turbines with dynamic and nonlinear characteristics. Similarly, Velasco-Gallego et al [42] developed an ensemble model combining a 1D-CNN and LSTM to predict the remaining useful lives of turbochargers in diesel generators.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multi-Layer Attention Mechanisms and CNN-LSTM for Fault Prediction in Marine Diesel Engines

Sun,

Ren,

Duan

et al. 2024

JMSE

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Timely and effective maintenance is imperative to minimize operational disruptions and ensure the reliability of marine vessels. However, given the low early warning rates and poor adaptability under complex conditions of previous data-driven fault prediction methods, this paper presents a hybrid deep learning model based on multi-layer attention mechanisms for predicting faults in a marine diesel engine. Specifically, this hybrid model first introduces a Convolutional Neural Network (CNN) and self-attention to extract local features from multi-feature input sequences. Then, we utilize Long Short-Term Memory (LSTM) and multi-head attention to capture global correlations across time steps. Finally, the hybrid deep learning model is integrated with the Exponential Weighted Moving Average (EWMA) to monitor the operational status and predict potential faults in the marine diesel engine. We conducted extensive evaluations using real datasets under three operating conditions. The experimental results indicate that the proposed method outperforms the current state-of-the-art methods. Moreover, ablation studies and visualizations highlight the importance of fusing multi-layer attention, and the results under various operating conditions and application scenarios demonstrate that this method possesses predictive accuracy and broad applicability. Hence, this approach can provide decision support for condition monitoring and predictive maintenance of marine mechanical systems.

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Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning

Cited by 8 publications

References 21 publications

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Fusion of Multi-Layer Attention Mechanisms and CNN-LSTM for Fault Prediction in Marine Diesel Engines

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