Gas Turbine Fault Classification Based on Machine Learning Supervised Techniques

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

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Fault classification in electrofusion polyethylene joints by combined machine learning, thermal pulsing and IR thermography methods – A comparative study

Infrared Physics & Technology ◽

10.1016/j.infrared.2018.11.032 ◽

2019 ◽

Vol 96 ◽

pp. 262-266

Author(s):

Marjan Doaei ◽

M. Sadegh Tavallali ◽

Hossein Nejati

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Fault Classification ◽

Ir Thermography

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Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

Applied Sciences ◽

10.3390/app10144965 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4965

Author(s):

Yordanos Dametw Mamuya ◽

Yih-Der Lee ◽

Jing-Wen Shen ◽

Md Shafiullah ◽

Cheng-Chien Kuo

Keyword(s):

Machine Learning ◽

Fault Detection ◽

Radial Distribution ◽

Fault Classification ◽

Discrete Wavelet ◽

Efficiency Coefficient ◽

Distribution Grid ◽

Phase Current ◽

Three Phase ◽

Types Of Faults

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.

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Machine Learning in Gas Turbines

Mechanical Engineering ◽

10.1115/1.2018-sep5 ◽

2018 ◽

Vol 140 (09) ◽

pp. S54-S55 ◽

Cited By ~ 1

Author(s):

V. Michelassi ◽

C. Allegorico ◽

S. Cioncolini ◽

A. Graziano ◽

L. Tognarelli ◽

...

Keyword(s):

Machine Learning ◽

Gas Turbine ◽

Gas Turbines ◽

Applications Of Machine Learning ◽

Selection Of

This paper describes a selection of Baker Hughes, a GE company (BHGE) activities to support Gas Turbine (GT) design and operation from simple to more elaborate applications of Machine Learning (ML).

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Combustion Instability Diagnosis Using Machine Learning Methodology of High-Speed Flame Images for the Safe Operation of a Gas Turbine Combustor

Transactions of the Korean Society of Mechanical Engineers B ◽

10.3795/ksme-b.2021.45.8.447 ◽

2021 ◽

Vol 45 (8) ◽

pp. 447-458

Author(s):

Byung Chul Jun ◽

Dae Jin Jang ◽

Min Chul Lee

Keyword(s):

Machine Learning ◽

Gas Turbine ◽

High Speed ◽

Combustion Instability ◽

Gas Turbine Combustor ◽

Safe Operation

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Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

10.1115/gt2021-58916 ◽

2021 ◽

Author(s):

Enzo Losi ◽

Mauro Venturini ◽

Lucrezia Manservigi ◽

Giuseppe Fabio Ceschini ◽

Giovanni Bechini ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Gas Turbine ◽

Gas Turbines ◽

Remaining Useful Life ◽

Training Data ◽

The Novel ◽

Novel Approach ◽

Forest Models ◽

Random Forest Models

Abstract A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble learning method consisting of an aggregation of decision tree classifiers. This paper presents a novel methodology aimed at exploiting information embedded in the data and develops Random Forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting. First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case-studies, involving filed data taken during three years of operation of two fleets of Siemens gas turbines located in different regions. The novel methodology allows values of Precision, Recall and Accuracy in the range 75–85 %, thus demonstrating the industrial feasibility of the predictive methodology.

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