Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer

A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault di...

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Bibliographic Details
Published in:	Mathematics (Basel) 2022-01, Vol.10 (1), p.80
Main Authors:	Khan, Asif, Kim, Jun-Sik, Kim, Heung Soo
Format:	Article
Language:	eng
Subjects:	actual systems Algorithms Artificial neural networks autonomous feature extraction Computer simulation computer simulations Damage detection Datasets Deep learning Fault diagnosis Feature extraction Knowledge Knowledge management Machine learning Machinery Neural networks Parameter identification Rotors Sensors transfer learning Working conditions
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Summary:	A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault diagnosis in an actual system. A simple shaft-disk system was used to generate a substantial set of source data for three health states of a rotor system, and that data was used to train, validate, and test a customized deep neural network. The deep learning model, pretrained on simulation data, was used as a domain and class invariant generalized feature extractor, and the extracted features were processed with traditional machine learning algorithms. The experimental data sets of an RK4 rotor kit and a machinery fault simulator (MFS) were employed to assess the effectiveness of the proposed approach. The proposed method was also validated by comparing its performance with the pre-existing deep learning models of GoogleNet, VGG16, ResNet18, AlexNet, and SqueezeNet in terms of feature extraction, generalizability, computational cost, and size and parameters of the networks.
ISSN:	2227-7390 2227-7390