Diagnosing hydraulic directional valve spool stick faults enabled by hybridized intelligent algorithms

The hydraulic directional valve represents a fundamental component of a hydraulic system. The severe operating environment could cause undesirable faults, with the spool stick being the particular concern. It will lead to a reduction in the overall performance of the operating system, even with the potential for failure. To address this issue, this study presents a hybrid intelligent algorithm-based diagnostic approach for the hydraulic directional valve spool stick fault to facilitate timely industrial inspection and maintenance. Firstly, the monitoring signals on hydraulic directional valves are denoised using wavelet packet denoising (WPD). Then, the denoised signals are decomposed via sparrow search algorithm (SSA) optimized for variational mode decomposition (VMD) in order to obtain a typical fault feature vector. Finally, a combined model of the convolutional neural network (CNN) and the long short-term memory (LSTM) is employed to diagnose the valve spool stick fault. The results of this study indicate that the proposed approach can reduce the signal processing time by 56.60%. The diagnostic accuracy of the approach is 97.01% and 96.24% for sensors located at different positions, and the accuracy of the fusion sensor group is 99.55%. These fault diagnostic performances provide a basis for further research into hydraulic directional valve spool stick fault and are appliable to other hydraulic equipment fault diagnosis applications.