Research Area:  Machine Learning

Abstract:

Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in practical engineering, the fault modes of rolling bearings are usually compound faults and contain a large amount of noise, which increases the difficulty of fault diagnosis. Therefore, a deep feature enhanced reinforcement learning method is proposed for the fault diagnosis of rolling bearing. Firstly, to improve robustness, the neural network is modified by the Elu activation function. Secondly, attention model is used to improve the feature enhanced ability and acquire essential global information. Finally, deep Q network is established to accurately diagnosis the fault modes. Sufficient experiments are conducted on the rolling bearing dataset. Test result shows that the proposed method is superior to other intelligent diagnosis methods.

Keywords:  
Rolling bearing
Rotating machinery
Reinforcement learning
Neural network
Activation function
Deep Q network

Author(s) Name:  Ruixin Wang,Hongkai Jiang,Ke Zhu

Publisher name:  Elsevier

DOI:  10.1016/j.aei.2022.101750

Volume Information:  Volume 54

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1474034622002087