Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine
Multi-fault diagnosis of rolling element bearing is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of nonstationarity and nonlinearity, the detection, extraction and classification of the fault feature turn into a...
Main Authors: | Qingbin Tong, Baozhu Han, Yuyi Lin, Weidong Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
JVE International
2016-11-01
|
Series: | Journal of Vibroengineering |
Subjects: | |
Online Access: | https://www.jvejournals.com/article/17090 |
Similar Items
-
A Fault Diagnosis Approach for Rolling Element Bearings Based on RSGWPT-LCD Bilayer Screening and Extreme Learning Machine
by: Qingbin Tong, et al.
Published: (2017-01-01) -
Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion
by: Iau-Quen Chung, et al.
Published: (2021-02-01) -
A Hybrid EEMD-Based SampEn and SVD for Acoustic Signal Processing and Fault Diagnosis
by: Zhi-Xin Yang, et al.
Published: (2016-04-01) -
A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
by: Peng Lu, et al.
Published: (2018-03-01) -
Properties of the total least squares estimation
by: Wang Leyang
Published: (2012-11-01)