Paper Info
Title
Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling.
Abstract
Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different sampling rates, which will inevitably result in a problem that a very small number of samples with a complete structure can be used for deep learning since the input of a deep neural network (DNN) is required to be a structurally complete sample. On the other hand, a large number of samples are required to ensure the efficiency of deep learning based fault diagnosis methods. To solve the problem that a structurally complete sample size is too small, this paper proposes a fault diagnosis framework of missing data based on transfer learning which makes full use of a large number of structurally incomplete samples. By designing suitable transfer learning mechanisms, extra useful fault features can be extracted to improve the accuracy of fault diagnosis based simply on structural complete samples. Thus, online fault diagnosis, as well as an offline learning scheme based on deep learning of multi-rate sampling data, can be developed. The efficiency of the proposed method is demonstrated by utilizing data collected from the QPZZ- II rotating machinery vibration experimental platform system.
Year
DOI
Venue
2019
10.3390/s19081826
SENSORS
Keywords
Field
DocType
fault diagnosis,DNN,transfer learning,missing data
Offline learning,Data mining,Transfer of learning,Feature extraction,Electronic engineering,Sampling (statistics),Artificial intelligence,Engineering,Deep learning,Missing data,Artificial neural network,Sample size determination
Journal
Volume
Issue
ISSN
19
8
1424-8220
Citations 
PageRank 
References 
1
0.36
0
Authors
3
Name
Order
Citations
PageRank
Danmin Chen172.16
Shuai Yang272.83
Funa Zhou3153.96