This paper applies a novel feature extraction method called Extended Phase Space Topology (EPST) in order to diagnose various faults in a gear-train system. The EPST method, that our research team has been developing, is based on characterizing the vibration data using the topology of phase space, computing its density distribution and then expanded in a series of orthogonal functions. The resulting coefficients are subsequently used in a machine learning algorithm. For this study, multiple test gears with different health conditions (such as a healthy gear and defective gears with root crack on one tooth, multiple cracks on five teeth and missing tooth) are studied. The vibration data of a gear-train is measured by a triaxial accelerometer installed on the test. Results indicate that EPST is efficient in diagnosing the status of the health of the gear system and characterizing the dynamic behavior. Moreover, the EPST procedure does not require a priori knowledge about the dynamics of the system. EPST needs no noise reduction, signal prepossessing, feature ranking or selection, and therefore can easily be applied in a relatively automated process.
How to Cite
feature extraction, gear fault detection, Machinery diagnostics
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