This paper presents a nonlinear time series analysis method for rotating machine damage detection and diagnostics. Specifically, the permutation entropy is investigated as a statistical measure for signal characterization. Through space reconstruction, the permutation entropy describes the complexity of the time series measured on a physical system, and takes its non-linear behavior into account. By identifying changes in the vibration signals measured on rotating machines, which are typical precursors of defect occurrence, permutation entropy can serve as a diagnostic tool. Experiments on a custom-designed gearbox system have confirmed its effectiveness for machine structural health monitoring applications.

This content is only available via PDF.
You do not currently have access to this content.