Dynamic States Recognition of Friction Noise in the Wear Process Based on Moving Cut Data-Approximate Entropy

Abstract

Wear experiments are performed to explore dynamic states changes of friction noise signals. A new characteristic parameter, moving cut data-approximate entropy (MC-ApEn), is adopted to quantitatively recognize dynamic states. Additionally, determinism (DET), one key parameter of recurrence quantification analysis, is applied to verify the reliability of recognition results of MC-ApEn. Results illustrate that MC-ApEn of friction noise has distinct changes in different wear processes, and it can accurately detect abrupt change points of dynamic states for friction noise. Furthermore, DET of friction noise rapidly declines first, then fluctuates around a small value, and finally increases sharply, which just corresponds to the evolution process of MC-ApEn. So, the reliability of wear state recognition on the basis of MC-ApEn can be confirmed. It makes it possible to accurately and reliably recognize wear states of friction pairs based on MC-ApEn.