HILBERT-HUANG TRANSFORM BASED ACOUSTIC EMISSION SIGNAL QUANTIFICATION FOR ROTATIONAL MACHINE HEALTH MONITORING AND DIAGNOSTICS

摘要

Acoustic emission (AE) based techniques are becoming an attracting alternatives for rotational machine health monitoring and diagnostics due to the advantages of the AE signals over the extensively used vibration signals. In the filed of rotational machine health monitoring and diagnostics, in comparison with vibration based methods, the AE based techniques are in their infant stage of the development. From the perspective of machine health monitoring and diagnostics, developing a systematic method for quantification of acoustic emission signals is important. In this paper, a Hilbert-Huang transform based acoustic emission signal quantification methodology for rotational machine health monitoring and diagnostics is presented. The methodology incorporates a threshold based de-noising technique into Hilbert-Huang transform to increase the signal-to-noise ratio. A gear fault detection case study is conducted on a notational split-torque gearbox using acoustic emission signals to demonstrate the effectiveness of the methodology.