Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on th...

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Bibliographic Details
Main Authors:	Tengyu Li, Ziming Kou, Juan Wu, Fen Yang
Format:	Article
Language:	English
Published:	MDPI AG 2021-06-01
Series:	Entropy
Subjects:	low-speed hoist bearing weak feature extraction iterative autocorrelation autocorrelation kurtosis entropy multipoint optimal minimum entropy deconvolution adjusted
Online Access:	https://www.mdpi.com/1099-4300/23/7/789

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