Fault identification in hydraulic valves is essential in maintaining the reliability and security of hydraulic systems. Due to the nonlinear characteristics of hydraulic systems under noisy working conditions, it is difficult to extract fault features from vibration signals collected from the surface of the valve body. Therefore, a DSmT-based three-layer method using multi-classifier is proposed to detect multiple faults occurred in hydraulic valves. Firstly, the raw signals are personalized to construct the training samples and the unknown testing samples. Secondly, a three-layer structure of the hybrid model called the layered hybrid model is constructed, which is suitable for hydraulic valves to detect the faults of different fault groups (including coil fatigue in the actuator and the abrasion inside the valve) and improve the diagnosis accuracy obviously. Finally, classification methods are selected to classify fault groups in the first two layers, and then the fault types are identified in the third layer by the fusion results using the Dezert-Smarandache Theory (DSmT). Experimental investigations are performed to validate the performance of the present method using a hydraulic valve (solenoid controlled pilot operated directional valve) controlled the hydraulic test rig. The results show that the average accuracy of detecting twelve types of faults is about 98.1%, which are better than those using other methods. It is expected that the present DSmT-based three-layer method using multi-classifier can be applied to more complex hydraulic systems.

液压阀中的故障识别对于维持液压系统的可靠性和安全性至关重要。由于液压系统在嘈杂的工作条件下具有非线性特性，因此很难从阀体表面收集的振动信号中提取故障特征。因此，提出了一种基于DSmT的多分类器三层方法来检测液压阀中的多个故障。首先，原始信号被个性化以构造训练样本和未知测试样本。其次，构建了混合模型的三层结构，称为分层混合模型，适用于液压阀，以检测不同故障组的故障（包括执行器中的线圈疲劳和阀内的磨损），并明显提高诊断准确性。最后，选择分类方法对前两层中的故障组进行分类，然后使用Dezert-Smarandache理论（DSmT）通过融合结果在第三层中识别故障类型。使用液压试验台控制的液压阀（电磁控制的先导方向阀）进行实验研究以验证本方法的性能。结果表明，十二种类型故障的平均检测准确率约为98.1％，优于其他方法。