A comparison study on the different SNR levels to the accuracy of two deep learning techniques in fault diagnosis of planetary gearbox
- Resource Type
- Conference
- Authors
- Wang, Ruiyuan; Wang, Kesheng
- Source
- 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) Advanced Reliability and Maintenance Modeling (APARM), 2020 Asia-Pacific International Symposium on. :1-6 Aug, 2020
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Nuclear Engineering
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Training
Fault diagnosis
Signal to noise ratio
Data models
Gears
Noise reduction
Planetary gearbox
De-noising Convolutional Autoencoder
Convolutional Neural Network
Anti-noise fault diagnosis
Deep learning
- Language
For most of the current deep learning models, the noise contaminated data are usually used in both model training and test. The ability of deep learning models to withstand different levels of noise becomes an important research topic. In this work, a recent developed deep learning model called DCAE-CNN is studied under different noise levels of training and test samples. The model combines a one-dimensional De-noising Convolutional Auto-encoder (DCAE-1D) and a one-dimensional Convolutional Neural Network (CNN-1D). The DCAE-1D network enables the noise reduction ability embedded in the model and may accommodate noisy training and test samples. The effectiveness of this model is studied by using different levels of SNR to planetary gearbox fault diagnosis. The DCAE-CNN and CNN model are compared by using different SNR noise contaminated training and test samples, the results demonstrated the effectiveness of the DCAE-CNN.