NDLI: New method of bearing fault diagnosis based on mmemd and DE_ELM

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New method of bearing fault diagnosis based on mmemd and DE_ELM

Content Provider	IET Digital Library
Author	Hu, Yongtao Dong, Mingru Wang, Guozhu Fan, Zheng Zhang, Shuqing
Abstract	A new method of bearing fault diagnosis based on multi-masking empirical mode decomposition (MMEMD) and extreme machine learning optimised by differential evolution algorithm (DE_ELM) is proposed in this study. MMEMD is an improvement of empirical mode decomposition (EMD). By adding masking signals to the signals to be decomposed in different levels, MMEMD can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. Differential evolution algorithm is applied to determine the parameters of ELM for improving the classification accuracy. The four parameters are determined at one time by uniformly coded as the individuals of the differential evolution algorithm. To achieve the bearing fault diagnosis, the fault signals are first decomposed into different intrinsic mode functions (IMFs) and the sample entropy of each IMF was calculated as the fault feature. Then the fault feature was divided into training set and testing set. Input the training set to the DE_ELM to obtain the fault classification model. Finally, the testing set was put into the model for fault diagnosis. The experiment and wind turbine bearing fault diagnosis results show that the method could identify the different bearing faults with high reliability and accuracy.
Starting Page	9152
Ending Page	9156
Page Count	5
Volume Number	2019
e-ISSN	20513305
Issue Number	Issue 23, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2019/23
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9206
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2019-03-25
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Civil And Mechanical Engineering Computing Data Handling Technique DE_ELM Differential Evolution Algorithm Digital Signal Processing Entropy Evolutionary Computation Extreme Machine Learning Fault Classification Model Fault Diagnosis Fault Feature Fault Signal Feedforward Neural Network High-frequency Component Hilbert Transform Integral Transforms Intrinsic Mode Function Knowledge Engineering Technique Learning in AI Low-frequency Component Machine Bearings Maintenance And Reliability Masking Signal Mechanical Component Mechanical Engineering Application of IT Mechanical Engineering Computing MMEMD Multimasking Empirical Mode Decomposition Neural Computing Technique Numerical Analysis Optimisation Optimisation Technique Pattern Classification Sample Entropy Signal Processing Signal Processing And Detection Wind Power Plant Wind Turbine Wind Turbine Bearing Fault Diagnosis
Content Type	Text
Resource Type	Article

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