<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Mechanical seal end faces are separated by a thin fluid film. The thickness of this film must be optimized so as to preventing the serious friction of two end faces and minimizing the fluid leakage. The micro scope condition monitoring of end face is of importance to ensure mechanical seals run normally. A method for measuring the film thickness of end faces and detecting the friction of end faces of mechanical seal has been presented in this paper. Eddy current sensors embedded in the stationary ring of mechanical seals are used to directly measure the thickness of the liquid-lubricated film. The Eddy current signal is decomposed by empirical mode decomposition into a series of intrinsic mode function. The information reflecting the film thickness is obtained by eliminating the false intrinsic mode function components. Acoustic emission sensor placed on the lateral of stationary ring is used to detect the friction of end faces. In order to decrease the acoustic emission signal’s noise, wavelet packet and kernel principal component analysis are used to extract the data features. Then cascaded decision is presented to improve the recognition rate of artificial neural network, by which the film thickness can be estimated accurately. With a set of tests, the results demonstrate that the method is effective. It can be widely used to take measurement of the film thickness in industrial field.</p>
</abstract>

Mechanical seal end faces are separated by a thin fluid film. The thickness of this film must be optimized so as to preventing the serious friction of two end faces and minimizing the fluid leakage. The micro scope condition monitoring of end face is of importance to ensure mechanical seals run normally. A method for measuring the film thickness of end faces and detecting the friction of end faces of mechanical seal has been presented in this paper. Eddy current sensors embedded in the stationary ring of mechanical seals are used to directly measure the thickness of the liquid-lubricated film. The Eddy current signal is decomposed by empirical mode decomposition into a series of intrinsic mode function. The information reflecting the film thickness is obtained by eliminating the false intrinsic mode function components. Acoustic emission sensor placed on the lateral of stationary ring is used to detect the friction of end faces. In order to decrease the acoustic emission signal’s noise, wavelet packet and kernel principal component analysis are used to extract the data features. Then cascaded decision is presented to improve the recognition rate of artificial neural network, by which the film thickness can be estimated accurately. With a set of tests, the results demonstrate that the method is effective. It can be widely used to take measurement of the film thickness in industrial field.

Film Thickness Measurement Of Mechanical Seal Based On Cascaded Artificial Neural Network Recognition Model

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mechanical seal end faces are separated by a thin fluid film. The thickness of this film must be optimized so as to preventing the serious friction of two end...

Film Thickness Measurement Of Mechanical Seal Based On Cascaded Artificial Neural Network Recognition Model

Publicado en línea: 01 dic 2014

Páginas: 1870 - 1889

Recibido: 22 ago 2014

Aceptado: 02 nov 2014

DOI: https://doi.org/10.21307/ijssis-2017-737

Palabras clave
Mechanical Seal, Film Thickness, Eddy Current, Acoustic Emission, Empirical Mode Decomposition, Artificial Neural Network

© 2014 Erqing Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Film Thickness Measurement Of Mechanical Seal Based On Cascaded Artificial Neural Network Recognition Model

Publicado en línea: 01 dic 2014

Páginas: 1870 - 1889

Recibido: 22 ago 2014

Aceptado: 02 nov 2014

DOI: https://doi.org/10.21307/ijssis-2017-737

Palabras claveMechanical Seal, Film Thickness, Eddy Current, Acoustic Emission, Empirical Mode Decomposition, Artificial Neural Network

© 2014 Erqing Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Mechanical Seal, Film Thickness, Eddy Current, Acoustic Emission, Empirical Mode Decomposition, Artificial Neural Network