Acoustic Emission Monitoring of Mechanical Seals Using MUSIC Algorithm based on Higher Order Statistics

This paper presents the use of the MUSIC algorithm improved by higher order statistics
(HOS) to extract key features from the noisy acoustic emission (AE) signals. The low signal-tonoise
ratio of AE signals has been identified as a main barrier to the successful condition
monitoring of pump mechanical seals. Since HOS methods can effectively eliminate Gaussian
noise, it is possible in theory to identify a change in seal conditions from AE measurements even
with low signal-to-noise ratios. Tests conducted on a test rig show that the developed algorithm can
successfully detect the AE signal generated from the friction of seal faces under noisy conditions.