Fault prediction of bearings based on LSTM and statistical process analysis

Author:

Liu Junqiang,Pan Chunlu,Lei Fan,Hu Dongbin,Zuo Hongfu

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference32 articles.

1. Deep-GBM-based fault diagnosis method for aero-engine intermediate bearing;Jing;Journal of Aeronautical Dynamics,2019

2. Research on Fault Diagnosis Technology for Rolling Bearings of Civil Aviation Engines;Yan;Industrial Technology,2017

3. Remaining Life Prediction of Equipment Using Interactive Multi-Model Algorithm;Jiwei;Journal of Air Force Engineering University: Natural Science Edition,2016

4. Summary of data-driven aero-engine fault diagnosis and prediction methods;Hongfu;Aviation Precision Manufacturing Technology,2012

5. Residual life predictions from vibration-based degradation signals: a neural network approach;Gebraeel;IEEE transactions on industrial electronics,2004

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