Simulation-Guided Interpretable Fault Diagnosis of Hydraulic Directional Control Valves Under Limited Fault Data Conditions

作者：Yi Xia, Aiping Xiao, Huafei Xiao, Xiangyi Zhao, Huijun Liu · 发表于：Sensors · 年份：2026 · DOI：10.3390/s26072052 · 被引用次数：1 · 研究领域：Hydraulic and Pneumatic Systems、Machine Fault Diagnosis Techniques、Fault Detection and Control Systems

Delayed switching faults in hydraulic directional control valves can significantly degrade system performance and reliability, yet their diagnosis remains challenging due to complex fault mechanisms and coupled sensor responses and limited fault samples in industrial applications. While data-driven approaches, including deep learning-based methods, have shown promising performance in fault diagnosis, their practical deployment in industrial quality inspection and condition monitoring is often constrained by limited fault data availability and insufficient physical interpretability of the diagnostic results. In this study, an interpretable fault diagnosis framework for delayed switching faults in hydraulic directional control valves is proposed based on a simulation-guided feature construction method and multi-pressure signal analysis. Instead of using simulation to generate synthetic training data, a physical simulation model is employed to analyze fault mechanisms and to guide the design of valve-level diagnostic features derived from inter-sensor pressure differences. These features are further evaluated using several classical machine learning classifiers, including RF, SVM, KNN, and LR under conditions of limited fault samples. Experimental results demonstrate that the proposed method effectively captures the structural imbalance caused by internal valve faults and achieves high diagnostic accuracy and robustness compared with conventional single-sensor approaches and pur...