Micro Gas Turbine fault detection and isolation with a combination of Artificial Neural Network and off-design performance analysis

Talebi, S.S.; Madadi, Ali; Tousi, Abolghasem M.; Kiaee, Mehrdad

doi:10.1016/j.engappai.2022.104900

Cited by 16 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the next stage, research is carried out aimed at researching the helicopter TE characteristics (4), taking into account the limitation of engine power depending on the gas-generator rotor r.p.m. (5). Research includes the gas-generator operating parameters and its effect on the overall engine power.…”

Section: Second Hidden Layermentioning

confidence: 99%

“…At the next stage, studies are carried out on helicopter TE characteristics ( 4), taking into account the limitation of engine power depending on the speed of the gas-generator rotor r.p.m. (5). By analyzing data on the gas-generator rotor r.p.m.…”

Section: Second Hidden Layermentioning

confidence: 99%

“…This optimization not only boosts engine performance and lifespan but also slashes maintenance costs and extends maintenance intervals [2,3]. Mathematical modelling is pivotal in analyzing and optimizing engine processes across various modes of helicopter TE operation, such as low gas mode, cruise modes, and emergency mode [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Modification of the neural network training algorithm to avoid local minima, which is one of the tasks that arise when optimizing neural networks. 5.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Vladov,

Yakovliev,

Bulakh

et al. 2024

Energies

View full text Add to dashboard Cite

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.

show abstract

Section: Second Hidden Layermentioning

confidence: 99%

Section: Second Hidden Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Modification of the neural network training algorithm to avoid local minima, which is one of the tasks that arise when optimizing neural networks. 5.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Vladov,

Yakovliev,

Bulakh

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…3 The data-based fault diagnostic method does not need an accurate mathematical mechanism model but only needs to make full use of the knowledge, experience of experts and existing data in the engine field. It includes many classic and typical machine learning methods, such as artificial neural networks (ANNs), [4][5][6][7] extreme learning machines (ELMs), 8 kernel recursive least squares (KRLS), support vector machines (SVMs) and so on. [9][10][11][12] Montazeri-Gh et al proposed a novel approach based on learning the fault characteristic maps of gas turbine components using an ELM.…”

Section: Introductionmentioning

confidence: 99%

Method of aeroengine fault diagnosis based on a Fingerprint map

Gou

Pan³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.

show abstract

Application of artificial intelligence in turbomachinery aerodynamics: progresses and challenges

Zou,

Xu,

Chen

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Turbomachinery plays a vital role in energy conversion systems, with aerodynamic issues being integral to its entire lifecycle, spanning the period of design, validation, and maintenance. Conventionally, the reliance on skilled aerodynamic engineers has been pivotal in the successful development of turbomachines. However, in the current era of burgeoning artificial intelligence (AI) technology, researchers are increasingly turning to AI to replace human expertise and decision-making in these aerodynamic issues and to solve previously intractable aerodynamic problems. This paper presents a systematic literature review of the latest advancements in applying AI to turbomachinery aerodynamics, encompassing the design, validation, and maintenance of compressors and turbines. It underscores how AI is revolutionizing the research paradigm of turbomachinery aerodynamics. AI’s powerful learning capability facilitates more precise and convenient aerodynamic analyses and inspires innovative aerodynamic design ideas that go beyond the capabilities of classical design techniques. Additionally, AI’s autonomous decision-making capability can be employed for aerodynamic optimization and active flow control of turbomachines, generating optimal aerodynamic solutions and complex control strategies that surpass human brains. As a main contribution, we provide a detailed exposition of the future intelligent turbomachinery research and development (R &D) system, along with highlighting potential challenges such as physics embedding, interactive 3D design optimization, and real-time prognoses. It is anticipated that harnessing AI’s full potential will lead to a comprehensive AI-based turbomachinery R &D system in the future.

show abstract

Micro Gas Turbine fault detection and isolation with a combination of Artificial Neural Network and off-design performance analysis

Cited by 16 publications

References 38 publications

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Method of aeroengine fault diagnosis based on a Fingerprint map

Application of artificial intelligence in turbomachinery aerodynamics: progresses and challenges

Contact Info

Product

Resources

About