Transfer Learning based Evolutionary Deep Neural Network for Intelligent
Fault Diagnosis
- URL: http://arxiv.org/abs/2109.13479v1
- Date: Tue, 28 Sep 2021 04:31:23 GMT
- Title: Transfer Learning based Evolutionary Deep Neural Network for Intelligent
Fault Diagnosis
- Authors: Arun K. Sharma, Nishchal K. Verma
- Abstract summary: The performance of a deep neural network (DNN) for fault diagnosis is very much dependent on the network architecture.
We propose an evolutionary Net2Net transformation (EvoNet2Net) that finds the best suitable DNN architecture for the given dataset.
We have used the Case Western Reserve University dataset and Paderborn university dataset to demonstrate the effectiveness of the proposed framework.
- Score: 11.427019313283997
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The performance of a deep neural network (DNN) for fault diagnosis is very
much dependent on the network architecture. Also, the diagnostic performance is
reduced if the model trained on a laboratory case machine is used on a test
dataset from an industrial machine running under variable operating conditions.
Thus there are two challenges for the intelligent fault diagnosis of industrial
machines: (i) selection of suitable DNN architecture and (ii) domain adaptation
for the change in operating conditions. Therefore, we propose an evolutionary
Net2Net transformation (EvoNet2Net) that finds the best suitable DNN
architecture for the given dataset. Nondominated sorting genetic algorithm II
has been used to optimize the depth and width of the DNN architecture. We have
formulated a transfer learning-based fitness evaluation scheme for faster
evolution. It uses the concept of domain adaptation for quick learning of the
data pattern in the target domain. Also, we have introduced a hybrid crossover
technique for optimization of the depth and width of the deep neural network
encoded in a chromosome. We have used the Case Western Reserve University
dataset and Paderborn university dataset to demonstrate the effectiveness of
the proposed framework for the selection of the best suitable architecture
capable of excellent diagnostic performance, classification accuracy almost up
to 100\%.
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