Rapid diagnosis of proportional valve pilot stage stiction fault via dynamic model-based temporal features

As the key flow control elements in modern mechatronic systems, electrohydraulic proportional valves determine the total system operating status. Due to effects such as mechanical wear, micro scale particles may accumulate in the oil and eventually cause stiction in the valve spool. Therefore, early prognosis of valve faults via spool displacement sensing is critical for efficient and economical operations. However, the spool stiction is difficult to measure due to the fully enclosed design of valves, excluding the possible sensor installation options. Traditionally, the sensor-less fault diagnosis is done by machine learning to classify the features extracted using frequency domain methods, which can be time consuming and the trained model is computationally too heavy for the onboard controller. In this paper, a light-weight spool stiction diagnosis method is proposed, where time-series features extracted from coil current signals as well as spool dynamics can be used to quantitively determine the stiction severeness. A hardware-in-the-loop test bed is designed to perform valve spool stiction simulating experiments, and a piloted operated solenoid proportional valve is selected as the validation target. A three-layer regression neural network is established as the baseline. Initial results show the proposed model can detect spool stiction at various degrees, which accuracies are on par with the baseline neural network but nearly three orders of magnitude faster.