Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration o...

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Bibliographic Details

Published in:	Sensors
Main Authors:	Lei Hu, Ligui Wang, Yanlu Chen, Niaoqing Hu, Yu Jiang
Format:	Article
Language:	English
Published:	MDPI AG 2022-09-01
Subjects:	rolling bearings fault diagnosis piecewise aggregate approximation CEEMDAN
Online Access:	https://www.mdpi.com/1424-8220/22/17/6599