Abstract
This paper proposes a high accuracy time-varying wear evolution method with cloud maps highlighting dynamic characteristics of surface signal. Firstly, the cloud map method is established by arrangement of measured data, thus time-varying wear evolution process is visualized on a series of plane figures for preliminary qualitative analysis. Then, high accuracy recognition of time-varying wear states is realized by cloud map shape parameters, including kurtosis and 1-D kernel density function highlighting distribution information of surface signal. The relative recognition degree by cloud map shape parameters are higher than those of friction coefficient, Root-Mean-Square (RMS) deviation and fractal dimension. Finally, high accuracy time-varying wear life prediction is realized by cloud map size growing speed highlighting waviness information of surface signal. The relative prediction error is reduced from more than 20% to less than 10% compared with friction coefficient and roughness.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
