<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.</p></abstract>

Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.

Pulsation Signals Analysis of Turbocharger Turbine Blades based on Optimal EEMD and TEO

Polish Maritime Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes...

Pulsation Signals Analysis of Turbocharger Turbine Blades based on Optimal EEMD and TEO

Published Online: Oct 18, 2019

Page range: 78 - 86

DOI: https://doi.org/10.2478/pomr-2019-0048

Keywords
urbocharger turbine blades, pulsation signals analysis, ensemble empirical mode decomposition, Teager energy operator, correlation kurtosis

© 2019 Fengli Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Pulsation Signals Analysis of Turbocharger Turbine Blades based on Optimal EEMD and TEO

Published Online: Oct 18, 2019

Page range: 78 - 86

DOI: https://doi.org/10.2478/pomr-2019-0048

Keywordsurbocharger turbine blades, pulsation signals analysis, ensemble empirical mode decomposition, Teager energy operator, correlation kurtosis

© 2019 Fengli Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
urbocharger turbine blades, pulsation signals analysis, ensemble empirical mode decomposition, Teager energy operator, correlation kurtosis