A Hybrid Approach Combining Data-Driven and Signal-Processing-Based Methods for Fault Diagnosis of a Hydraulic Rock Drill

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.