Development of entropy measure for selecting highly sensitive WPT band to identify defective components of an axial piston pump

This paper proposes a novel tangent hyperbolic fuzzy entropy measure-based method for determining the highly (most) sensitive frequency band to easily identify defective components in an axial piston pump. The validation of proposed scheme was done by experimental and simulation analysis. In the pro-posed method, initially, the raw vibration signal is decomposed into 16 frequency bands. Second, the energy of the WPT band is computed. Third, lower bounds of energy readings at the 4th level of decom-position are extracted, and thereafter they are normalised and finally converted into the forms of fuzzy sets. Following that, the proposed measure's value is computed. The proposed measure is analogous to the quantity of available information. The WPT4 band with the highest measure value will have the most information and can be designated as sensitive. Finally, envelope demodulation of the sensitive band is computed to identify the defects by evaluating the magnitude of vibration at fundamental frequencies. The superiority of the proposed sensitive band selection technique has been justified by comparing the findings with the widely used Fast-Kurtogram, Protugram, and Spectral-Kurtosis methods. The authentic-ity of the proclaimed tangent hyperbolic fuzzy entropy measure is accomplished with the existing Deluca and Termini fuzzy entropy measures. The latter entropy measure exhibited some inconsequential and unspecified results and established the superiority of our defect identification methodology.(c) 2023 Elsevier Ltd. All rights reserved.