Correlation tests of the engine performance parameter by using the detrended cross-correlation coefficient

Dong, Kuiran; Gao, You; Jing, Liming

doi:10.3938/jkps.66.539

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…The researched aero engine is a low bypass ratio turbofan engine, which is mainly composed of the fan, compressor, combustor, high-pressure turbines, and low-pressure turbines. There is a complex coupling relationship between these components [17]. Studies [18] have shown that the performance degradation of the engine is mainly reflected in the change in the component structure (such as blade tip drop, blade corrosion, etc.)…”

Section: Problem Descriptionmentioning

confidence: 99%

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Qin¹,

Zhao²,

Ren³

et al. 2022

Sustainability

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A low bypass ratio turbofan engine operates in a hostile environment, resulting in performance degradation. This seriously affects the security and reliability of the engine. Therefore, a performance degradation evaluation method for engines based on flight data is proposed. The method expands the equation system to solve the underdetermined problem caused by the lack of engine sensors based on multiple operating point analysis. The improved evolution algorithm is employed to solve the equation system, which relieves the problem of insufficient precision. The engine performance degradation dataset is established based on the engine performance calculation model to verify the reliability of the degradation evaluation method. The results show that the method is applicable to the dataset. Finally, the method is applied to the actual flight data to study the law of the performance degradation of the researched engine, which indicates that the engine’s fan efficiency and high-pressure compressor flow capacity have an apparent downward trend over time.

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Section: Problem Descriptionmentioning

confidence: 99%

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Qin¹,

Zhao²,

Ren³

et al. 2022

Sustainability

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“…e DCCA method is widely applied to measure the cross-correlations in different fields, such as social sciences [7], biology [8], climatology [9], geophysics [10,11], transportation [12,13], seismic signals [11,14], economics [15][16][17][18][19][20], and aeroengine dynamics [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Multifractal Detrended Partial Cross-Correlation Analysis for the Correlation Estimator

Dong

Gao

2020

Complexity

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In this paper, we develop a new method to measure the nonlinear interactions between nonstationary time series based on the detrended cross-correlation coefficient analysis. We describe how a nonlinear interaction may be obtained by eliminating the influence of other variables on two simultaneous time series. By applying two artificially generated signals, we show that the new method is working reliably for determining the cross-correlation behavior of two signals. We also illustrate the application of this method in finance and aeroengine systems. These analyses suggest that the proposed measure, derived from the detrended cross-correlation coefficient analysis, may be used to remove the influence of other variables on the cross-correlation between two simultaneous time series.

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“…Furthermore, in many real-world applications, complex systems controlled by regulatory mechanisms that operate on different time scales, generate time series that exhibit highly variable fluctuations at multiple levels of resolution [ 22 , 23 , 24 , 25 ]. In order to investigate the multiscale characteristic, Gao et al proposed a multiscale limited penetrable HVG for analyzing a single nonlinear time series and constructed a multiscale complex network from multivariate time series [ 5 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency

Dong

Che

Zou

2019

Entropy

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The horizontal visibility graph is not only a powerful tool for the analysis of complex systems, but also a promising way to analyze time series. In this paper, we present an approach to measure the nonlinear interactions between a non-stationary time series based on the horizontal visibility graph. We describe how a horizontal visibility graph may be calculated based on second-order and third-order statistical moments. We compare the new methods with the first-order measure, and then give examples including stock markets and aero-engine performance parameters. These analyses suggest that measures derived from the horizontal visibility graph may be of particular relevance to the growing interest in quantifying the information exchange between time series.

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Correlation tests of the engine performance parameter by using the detrended cross-correlation coefficient

Cited by 11 publications

References 19 publications

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Higher-Order Multifractal Detrended Partial Cross-Correlation Analysis for the Correlation Estimator

Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency

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