A Hybrid Approach Combining Data-Driven and Signal-Processing-Based Methods for Fault Diagnosis of a Hydraulic Rock Drill
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Published:2023-07-10
Issue:1
Volume:14
Page:
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ISSN:2153-2648
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Container-title:International Journal of Prognostics and Health Management
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language:
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Short-container-title:IJPHM
Author:
Oh Hye Jun,Yoo Jinoh,Lee Sangkyung,Chae Minseok,Park Jongmin,Youn Byeng D
Abstract
This study presents a novel method for fault diagnosis of a hydrostatic rock drill. Hydraulic rock drills suffer from both domain discrepancy issues that arise due to their harsh working environment and indivisible difference. As a result, fault diagnosis is very challenging. To overcome these problems, we propose a novel diagnosis method that combines both data-driven and signal-process-based methods. In the proposed approach, data-driven methods are employed for overall fault classification, using domain adaptation, metric learning, and pseudo-label-based deep learning methods. Next, a signal-process-based method is used to diagnose the specific fault by generating a reference signal. Using the combined approach, the fault-diagnosis performance was 100%; the proposed method was able to perform well even in cases with domain discrepancy.
Subject
Mechanical Engineering,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,Civil and Structural Engineering,Computer Science (miscellaneous)