Shaft Orbit Feature Based Rotator Early Unbalance Fault Identification

Feature extraction is crucial to rotating machinery prognosis, which is an important aspect of condition monitoring as well as maintenance program, since the quality of feature will impact the result significantly. Vibration signals are commonly used as the source for feature extraction during the prognosis process, especially the energy feature of fundamental frequency (which is written as 1X), 2X, 3X, 1/2X, etc. Yet this kind of feature shows insufficiency for identifying stages of performance degradation and classifying the type of early fault, therefore researchers focused mainly on improving the methods of feature extraction to solve this problem. However, features extracted from vibration signals always ignore some fault information such as kinematics information and phase information, thus other source of feature is needed to provide supplement or even substitute for higher efficiency and sharpness of separation in rotating machinery prognosis, which are strongly demanded by today's complex and advanced machines. This paper introduced one kind of classic feature source: shaft orbit, Which is widely used in traditional diagnosis for failure classification, into prognosis, and its effectiveness is verified in rotor early unbalance fault identification using features extracted from it, compared with energy features of frequency hand extracted from vibration signals. Result shows that shaft orbit feature can be used in identifying different early fault stages of rotor unbalance, which indicates that utilizing shaft orbit as source of feature extraction can provide a new approach of getting early fault features in rotating machinery prognosis. (C) 2016 Published by Elsevier B.V.