DiagNet: Machine Fault Diagnosis Using Federated Transfer Learning in Low Data Regimes

Page view(s)
11
Checked on Sep 22, 2022
DiagNet: Machine Fault Diagnosis Using Federated Transfer Learning in Low Data Regimes
Title:
DiagNet: Machine Fault Diagnosis Using Federated Transfer Learning in Low Data Regimes
Other Titles:
AAAI 2022 FL Workshop
DOI:
Keywords:
Publication Date:
02 March 2022
Citation:
NA
Abstract:
Data-driven fault diagnosis plays a key role in reducing maintaining costs and reducing down time for industrial machines. Deep learning has shown promising performance in identifying the different fault types. Yet, large amount of data is required to achieve satisfactory performance. In the real world, fault data is often rare, thus there is incentive for different corporations to work together to train a fault detection model. However, sharing data between different factories may not be applicable due to the data privacy concerns. Besides, distribution of data collected from different entities can be non i.i.d. As a results, a model trained on one machine can fail to generalise to different machines due to the distribution shift problem. In this work, we propose DiagNet, a federated transfer learning framework for machine fault diagnosis tasks. Specifically, to address the data privacy concerns, we employ the federated learning approach by jointly training a global model across multiple clients without sharing their raw data. However, the global model does not perform the best for each of the clients due to data distribution variances. To further tackle this problem, we employ the transfer learning approach to adapt the global model separately on each client with his own private machine data. Experimental results under low data regimes show that our DiagNet framework can significantly improve the fault-diagnosis model training accuracy by up to 28%.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Advanced Manufacturing and Engineering (AME) Programmatic Programme
Grant Reference no. : A19E3b0099
Description:
ISBN:

Files uploaded:

Files uploaded:

File Size Format Action
fl-aaai-22-diagnet.pdf 321.25 KB PDF Open