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

Yong Li, Gang Cheng, Xihui Chen, Yusong Pang

Research output: Contribution to journalArticleScientificpeer-review

30 Downloads (Pure)

Abstract

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%.
Original languageEnglish
Article number1025
Number of pages15
JournalEntropy
Volume21
Issue number10
DOIs
Publication statusPublished - 2019

Keywords

  • bearing
  • fault diagnosis
  • MOMLMEDA
  • filter feature
  • LSTM

Fingerprint

Dive into the research topics of 'Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM'. Together they form a unique fingerprint.

Cite this