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

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

Li, Yong (China University of Mining and Technology)
Cheng, Gang (China University of Mining and Technology)
Chen, Xihui (Hohai University)
Pang, Y. (TU Delft Transport Engineering and Logistics)

2019

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%.

bearing
fault diagnosis
MOMLMEDA
filter feature
LSTM

http://resolver.tudelft.nl/uuid:fc594e2b-f880-493a-a5e2-3053441a6a10

https://doi.org/10.3390/e21101025

1099-4300

Entropy: international and interdisciplinary journal of entropy and information studies, 21 (10)

Institutional Repository

journal article

© 2019 Yong Li, Gang Cheng, Xihui Chen, Y. Pang