Intelligent fault diagnosis methods toward gas turbine: A review

LIU, Xiaofeng; CHEN, Yingjie; XIONG, Liuqi; WANG, Jianhua; LUO, Chenshuang; ZHANG, Liming; WANG, Kehuan

doi:10.1016/j.cja.2023.09.024

Cited by 6 publications

(2 citation statements)

References 145 publications

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“…A great variety of algorithms for each PHM stage have been separately developed in recent years, taking advantage of the progress in machine learning and deep learning research. Comprehensive reviews about this progress, such as [4][5][6][7], can be found in the literature. Recently, researchers have focused on deep learning methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in order to exploit their powerful feature learning and classification/prediction capabilities for use within PHM strategies in rotating machinery [8], in particular for aircraft engines.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

Pérez-Ruiz,

Tang,

Loboda

et al. 2024

Aerospace

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In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management systems to detect, identify, and forecast complex faults in a short time. Furthermore, it is necessary to ensure that these systems preserve their capabilities over time despite engine deterioration. This paper addresses these necessities by proposing an integrated system that considers the joint operation of feature extraction, anomaly detection, fault identification, and prognostic algorithms for engines with long operation times. To effectively reveal the actual engine condition, light adaptive degraded engine models are computed along with different health indicators that are used as inputs to train and test recognition and prediction models. The system is developed and evaluated using a specialized NASA platform which provides data from a turbofan engine fleet simultaneously experiencing long-term performance deterioration and faults. Contrary to other compared solutions, our results show that the proposed system is robust against the effects of engine deterioration, maintaining its level of detection, recognition, and prediction accuracy over a total engine service life. The low computational cost algorithms has generally fast performance in all stages, making the system suitable for online applications.

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Section: Introductionmentioning

confidence: 99%

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

Pérez-Ruiz,

Tang,

Loboda

et al. 2024

Aerospace

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show abstract

“…However, this method lacks accuracy due to limited consideration of external factors and real operating conditions. Polynomial approximation of bench test results is another approach [18,19], requiring extensive data and resulting in a cumbersome model when different operating modes are approximated separately. Due to how costly, limited, and noisy experimental data are, fuzzy inference systems [20,21] and neural networks [22,23] are effective.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Vladov,

Yakovliev,

Bulakh

et al. 2024

Energies

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The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines’ optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times.

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Application of a surrogate model for condition monitoring of a digital twin gas turbine

Luan,

Li,

Cao

et al. 2025

Applied Mathematical Modelling

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Intelligent fault diagnosis methods toward gas turbine: A review

Cited by 6 publications

References 145 publications

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Application of a surrogate model for condition monitoring of a digital twin gas turbine

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