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

Asif Khan, Jun-Sik Kim and Heung Soo Kim
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Asif Khan: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1 Gil, Jung-gu, Seoul 04620, Korea
Jun-Sik Kim: Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi-si 39177, Korea
Heung Soo Kim: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1 Gil, Jung-gu, Seoul 04620, Korea

Mathematics, 2021, vol. 10, issue 1, 1-26

Abstract: 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.

Keywords: computer simulations; actual systems; deep learning; transfer learning; autonomous feature extraction; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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