Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Kiakojoori, S.; Khorasani, K.

doi:10.1007/s00521-015-1990-0

Cited by 79 publications

(26 citation statements)

References 72 publications

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“…Despite the considerable amount of research in this field [1][2][3], information technology for control and diagnostics of the technical condition of aviation engines is not perfect for a number of reasons: on the one hand, weak information "link", lack of elements of "intelligence", allowing to rapidly, efficiently and effectively support responsible decision-making and, as a consequence, reduce the total time spent on maintenance of aviation engines; on the other hand, the unsteadiness of physical processes in the aviation engine, the complexity of their mathematical description, the dependence of its technical characteristics on external conditions of work, the limited composition of the measured parameters, their technological spread, etc. These factors lead to the need to automate the decision-making process on the technical condition of the aircraft engine under uncertainty.…”

Section: Problem Statementmentioning

confidence: 99%

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Vladov¹,

Kotliarov²,

Hrybanova³

et al. 2020

Вісник Херсонського національного технічного університету

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The subject of the study in the article is the modes of operation of the aircraft engine TV3-117 and methods of their control and diagnostics. The purpose of the work is to develop methods of control and diagnostics of the technical condition of the aircraft engine TV3-117 on the basis of neural network technologies in real time. The following tasks are solved: substantiation of the preconditions of the use of neural networks in the task of control and diagnostics of the technical condition of the aircraft engine TV3-117, construction of the generalized neural network and the choice of the algorithm for its training, the solution of the task of controlling the parameters of the technical condition of the aircraft engine TV3-117 with the use of neural networks. The following methods are used: methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the theory of information systems and data processing. The following results were obtained: The feasibility of using neural networks in the task of controlling and diagnosing the technical condition of the aircraft engine TV3-117 was substantiated. The expediency of developing neural networks based on the NN Predictive Controller. The expediency of using the gradient method of teaching neural networks is substantiated, as well as the method of training a neuro-regulator based on a neuro-modulator with the use of the method of reverse error propagation. The expediency of using the gradient method of teaching neural networks is substantiated, as well as the method of training a neuro-regulator based on a neuromodulator with the use of the method of reverse error propagation. The solution of the task of controlling the parameters of the technical condition of the aircraft engine ТВ3-117, which confirms the expediency of using neural networks in the task of control and diagnostics of the technical condition of the aircraft engine TV3-117, is obtained. Conclusions: The application of neural network technologies is effective in solving a wide range of poorly formalized tasks, one of which is the task of controlling the technical condition of the aircraft engine TV3-117. The advantage of neural networks in their application in the tasks of control and diagnostics of the technical condition of the aircraft engine TV3-117 is the possibility of working with small training samples, the appointment of soft tolerances, using the experience of experts to assess the technical condition of the aircraft engine TV3-117, which is important in the condition's information incompleteness.

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Section: Problem Statementmentioning

confidence: 99%

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Vladov¹,

Kotliarov²,

Hrybanova³

et al. 2020

Вісник Херсонського національного технічного університету

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“…These methods can be roughly divided into three categories: knowledge based, model based, and data based. Some expert systems and fuzzy logic [7][8][9] are the method based on knowledge. This method can make use of expert knowledge and experience, and it does not need to be very accurate model.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

Chen¹,

Han²,

Lei³

et al. 2017

Shock and Vibration

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Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.

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“…are among the most common causes of degradations in an engine. Then, the thermodynamic performance of the turbofan engine can be changed by these degradations [25]. The four rotating components: the fan, the compressor, the high pressure turbine, and the low pressure turbine are the main fault components [26].…”

Section: Turbofan Engine Modelmentioning

confidence: 99%

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

Lü

Huang

2018

Energies

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Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components' performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg-Marquardt (LM) algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF) method, the particle filter (PF) method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

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Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Cited by 79 publications

References 72 publications

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

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