Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transferopen access
- Authors
- Khan, Asif; Kim, Jun-Sik; Kim, Heung Soo
- Issue Date
- Jan-2022
- Publisher
- MDPI
- Keywords
- computer simulations; actual systems; deep learning; transfer learning; autonomous feature extraction; machine learning
- Citation
- MATHEMATICS, v.10, no.1
- Journal Title
- MATHEMATICS
- Volume
- 10
- Number
- 1
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20808
- DOI
- 10.3390/math10010080
- ISSN
- 2227-7390
- 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.
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