Degradation Trend Analysis Based on Data-Driven Health Modeling for an Electro-Hydraulic Drive System in Tunnel Drilling Rigs

An electro-hydraulic drive system is essential for the stable operation of tunnel drilling rigs in underground coal mines. However, components such as pumps, valves, and controllers inevitably experience gradual degradation under long-term and high-load conditions. Conventional monitoring approaches often rely on labeled fault data or suffer from limited interpretability, restricting their applicability in real engineering environments. To overcome these limitations, this study proposes an unsupervised degradation trend analysis method that does not use labeled samples. A sliding-window strategy was adopted to extract key statistical features. Principal component analysis was then employed to construct a unified health index, and Z-score normalization enabled the interpretable detection of abnormal tendencies in individual features. Validation on real drilling data revealed clear degradation behaviors, such as main pump leakage and control current drift, demonstrating that the proposed method offered a lightweight and interpretable solution for trend-based condition monitoring and provided practical support for the intelligent maintenance of electro-hydraulic drive systems.