Non-destructive diagnostic of aircraft engine blades by Fuzzy Decision Tree

Rabcan, Jan; Levashenko, Vitaly; Zaitseva, Elena; Subbotin, Sergey

doi:10.1016/j.engstruct.2019.109396

Cited by 31 publications

(12 citation statements)

References 29 publications

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“…An analysis of the diagnostic of effective power and efficiency of the GTU was proposed in [7][8][9]. In recent years, the usage of computer neural networks has been growing for parametric analysis of the technical systems state [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Stepanov

Zhirnov

Sazonkin

et al. 2022

Sensors

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In this article, we study the possibility of gas turbine unit (GTU) monitoring using interferometric fiber optic sensors. We used the Mach–Zehnder interferometer (MZI) scheme, which can be easily implemented and simply installed on the turbine, and also allows us to solve the problem of phase unwrapping conveniently. In this research, the following main steps were carried out: an experimental scheme based on the MZI was assembled, and its sensitive arm was fixed on the GTU under study; data on various operation modes of the GTU was collected; the data were subjected to frequency FFT analysis, based on which the main rotational speeds of the turbine were identified. With FFT analysis, we also demonstrated multiples harmonics, which appear in the case of GTU after operating time, caused by the number of blades. The possibility of GTU monitoring and analysis using a non-invasive compact fiber-optic sensor is demonstrated: spectral analysis is used to detect the rotor speed, as well as the presence or absence of high-order multiple frequencies indicating blade and bearing defects, which are determined by the number of GTU’s blades and rolling bearing used as turbines rotor supports.

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Section: Introductionmentioning

confidence: 99%

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Stepanov

Zhirnov

Sazonkin

et al. 2022

Sensors

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“…With above deficiencies, a single characteristic index is unable to fully characterize the running status characteristics, an comprehensive analysis of the status feature is needed. Considering fuzzy clustering analysis is based on the similarity between sample objects [21], and it has the characteristics of small sample analysis and does not require advanced training, which has also been widely applied to the status diagnosis of aero-engine and rolling bearing equipment [22,23].…”

Section: Introductionmentioning

confidence: 99%

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Zhu¹,

Dong

2021

IEEE Access

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The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train's operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer.INDEX TERMS Onboard traction transformer; running status diagnosis; insulation aging; kernel principal component analysis; fuzzy clustering

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“…The fuzzy clustering analysis algorithm is based on statistical theory [12], which intuitively presents the data classification in the form of a dynamic cluster diagram. It has the characteristics of small sample analysis, does not require advanced training, and has been successfully applied in the state diagnosis of the transformer, aero-engine, and other equipment [13,14]. Based on the above analysis, this paper proposes a VMD combined with KPCA to extract the power curve features of S700K turnout, and at the same time, fuzzy clustering analysis is used to realize the algorithm of running state diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Running Status Diagnosis of S700K Turnout Based on VMD-KPCA and Fuzzy Clustering

Li¹,

Wei²,

Wu³

et al. 2021

EJEE

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S700K turnout is the key equipment of railway line conversion. The diagnosis of S700K turnout in a normal, sub-health, and fault running state is the primary premise to ensure the safe operation of the railway. Aiming at the consistency between the characteristics of the power curve of S700K turnout and its state information, this paper proposes a new algorithm based on variational mode decomposition (VMD) and kernel principal component analysis (KPCA) to extract the characteristics of the power curve of S700K turnout. It uses fuzzy clustering analysis to diagnose the running state of S700K turnout. First, to extract the detailed components of the action power curve, it is decomposed into intrinsic mode function with limited bandwidth (BIMF) by VMD. Secondly, the multi-scale permutation entropy (MPE) is used to characterize the signal complexity of the power curve and different BIMF components, which are taken as the running state feature set. After KPCA analysis, eigenvalues with a contribution rate greater than 95% are selected as the state eigenvector. The experimental results show that the diagnosis algorithm can effectively identify the running state of S700K turnout, meet the characteristics of fewer fault samples of S700K turnout, and do not need to train in advance, which is of great significance for field guidance.

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Non-destructive diagnostic of aircraft engine blades by Fuzzy Decision Tree

Cited by 31 publications

References 29 publications

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Running Status Diagnosis of S700K Turnout Based on VMD-KPCA and Fuzzy Clustering

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