One- Dimensional Convolutional Neural Networks Based on Exponential Linear Units for Bearing Fault Diagnosis
- Resource Type
- Conference
- Authors
- Kong, Hanyang; Yang, Qingyu; Zhang, Zhiqiang; Nai, Yongqiang; An, Dou; Liu, Yibo
- Source
- 2018 Chinese Automation Congress (CAC) Automation Congress (CAC), 2018 Chinese. :1052-1057 Nov, 2018
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Rolling bearings
Convolution
Feature extraction
Fault diagnosis
Training
Convolutional neural networks
Deep learning
One-dimensional convolutional neural network
Rolling bearing
Exponential linear units
- Language
Rolling bearings are one of the most commonly used components in rotating machinery which is mainly operated in complex working environment. Therefore, it is of great theoretical value and practical significance to study the state monitoring and fault diagnosis technology of rolling bearing to avoid sudden accidents and make a better system maintenance. In this paper, we propose a one-dimensional convolutional neural network to identify rolling bearing fault. Furthermore, we adopt a novel activation function: exponential linear units in the task of rolling bearing fault diagnosis. Simulation results show that one-dimensional convolutional neural network has a prominent generalization ability and high accuracy rate. Exponential linear units can make neural network more robust and stable when we diagnose the rolling bearing fault.