An effective method for monitoring rolling bearings wear status via the application of noise assisted hilbert-huang transform

摘要

© 2021, Scibulcom Ltd.. All rights reserved.Empirical methods based on operator’s experience are very important in industry. Replacing them by more scientific methods, add values to these processes. This work investigates the application of a post-processing method for extracting spectra from vibration signals to detect faults of rolling-element bearings. Rolling-element bearings are fundamental components of rotating machinery. Faults of rolling-elements bearings are responsible for a substantial proportion of machine failures and therefore fault detection is important for improving the mechanical system reliability and the performance. Although rolling bearings have been investigated in detail in past studies, an innovative application of time-frequency analysis method, called complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), that overcomes known limitations of the conventional empirical mode decomposition concerning mode mixing and frequency separation, is represented. To validate this method, rolling-element bearings with known and localized faults are used to acquire datasets either from an experimental rig that stimulates a rotating machinery and from literature. The results verify the ability of the method to efficiently detect degradations of rolling-element bearings.