Jinoh Yoo Sangkyung Lee Minseok Chae Jongmin Park Byeng D Youn
This study presents a method for fault diagnosis of the hydrostatic rock drill. Hydraulic rock drill suffers from domain discrepancy due to harsh environment and indivisible difference, which leads to difficulty in diagnosing fault. To overcome these problems, we propose a fusion method of data-driven-based method and signal process-based method. In the case of the data-driven based method, the overall fault classification was performed using domain adaptation, metric learning, and pseudo label-based deep learning methods, and the signal process-based method was diagnosed for a specific fault by generating a reference signal. As a result, the fault diagnosis performance was 100%, and it was able to perform well even in domain discrepancy.
How to Cite
Fault Diagnosis, Hydraulic Rock Drill, Hybrid Approach, Domain Adaptation
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.