Next Article in Journal
A High Spectral Entropy (SE) Memristive Hidden Chaotic System with Multi-Type Quasi-Periodic and its Circuit
Previous Article in Journal
Multiscale Entropy of Cardiac and Postural Control Reflects a Flexible Adaptation to a Cognitive Task
Previous Article in Special Issue
A Fault Detection Method Based on CPSO-Improved KICA
Open AccessArticle

Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM

School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 Delft, The Netherlands
Author to whom correspondence should be addressed.
Entropy 2019, 21(10), 1025;
Received: 27 September 2019 / Revised: 16 October 2019 / Accepted: 20 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
As the supporting unit of rotating machinery, bearing can ensure efficient operation of the equipment. Therefore, it is very important to monitor the status of bearings accurately. A bearing fault diagnosis mothed based on Multipoint Optimal Minimum Local Mean Entropy Deconvolution Adjusted (MOMLMEDA) and Long Short-Term Memory (LSTM) is proposed. MOMLMEDA is an improved algorithm based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). By setting the local kurtosis mean as a new selection criterion, it can effectively avoid the interference of false kurtosis caused by noise and improve the accuracy of optimal kurtosis position. The optimal filter designed by optimal kurtosis position has periodic and amplitude characteristics, which are used as the fault feature in this paper. However, this feature has temporal characteristics and cannot be used as input of general neural network directly. LSTM is selected as the classification network in this paper. It can effectively avoid the influence of the temporal problem existing in feature vectors. Accurate diagnosis of bearing faults is realized by training classification neural network with samples. The overall recognition rate is up to 93.50%. View Full-Text
Keywords: bearing; fault diagnosis; MOMLMEDA; filter feature; LSTM bearing; fault diagnosis; MOMLMEDA; filter feature; LSTM
Show Figures

Figure 1

MDPI and ACS Style

Li, Y.; Cheng, G.; Chen, X.; Pang, Y. Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM. Entropy 2019, 21, 1025.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop