Version 1
: Received: 20 May 2020 / Approved: 22 May 2020 / Online: 22 May 2020 (05:21:56 CEST)
How to cite:
Smith, G.; Shibatani, M. Fault Diagnosis of Gearbox with a Transferable Deep Neural Network. Preprints2020, 2020050347. https://doi.org/10.20944/preprints202005.0347.v1
Smith, G.; Shibatani, M. Fault Diagnosis of Gearbox with a Transferable Deep Neural Network. Preprints 2020, 2020050347. https://doi.org/10.20944/preprints202005.0347.v1
Smith, G.; Shibatani, M. Fault Diagnosis of Gearbox with a Transferable Deep Neural Network. Preprints2020, 2020050347. https://doi.org/10.20944/preprints202005.0347.v1
APA Style
Smith, G., & Shibatani, M. (2020). Fault Diagnosis of Gearbox with a Transferable Deep Neural Network. Preprints. https://doi.org/10.20944/preprints202005.0347.v1
Chicago/Turabian Style
Smith, G. and Masayoshi Shibatani. 2020 "Fault Diagnosis of Gearbox with a Transferable Deep Neural Network" Preprints. https://doi.org/10.20944/preprints202005.0347.v1
Abstract
In the past years, various intelligent machine learning and deep learning algorithms have been developed and widely applied for gearbox fault detection and diagnosis. However, the real-time application of these intelligent algorithms has been limited, mainly due to the fact that the model developed using data from one machine or one operating condition has serious diagnosis performance degradation when applied to another machine or the same machine with a different operating condition. The reason for poor model generalization is the distribution discrepancy between the training and testing data. This paper proposes to address this issue using a deep learning based cross domain adaptation approach for gearbox fault diagnosis. Labelled data from training dataset and unlabeled data from testing dataset is used to achieve the cross-domain adaptation task. A deep convolutional neural network (CNN) is used as the main architecture. Maximum mean discrepancy is used as a measure to minimize the distribution distance between the labelled training data and unlabeled testing data. The study proposes to reduce the discrepancy between the two domains in multiple layers of the designed CNN to adapt the learned representations from the training data to be applied in the testing data. The proposed approach is evaluated using experimental data from a gearbox under significant speed variation and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other domain adaptation methods demonstrates the superiority of the proposed method.
Keywords
deep learning; maximum mean discrepancy; gearbox; fault detection
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
